A method and system for detecting defects on a rail surface

Abstract

The present application relates to a kind of defect detection methods and systems of rail surface, by the incident angle of different positions of rail surface and the dynamic disturbance of controllable illumination intensity, the reflection response sequence of rail surface under multiple illumination conditions is collected, the micro-optical response behavior of rail surface material is characterized;For each micro area of rail surface, calculate dynamic reflection stability, mark the area of stability anomaly as defect candidate area;Analyze the difference between defect candidate area and normal wheel-rail contact trace, combined with optical heterogeneity boundary feature, identify whether there is abnormal residue or non-normal wear boundary due to defect evolution, verify the authenticity of defect;For the defect candidate area that has been determined, in the direction of contact track of rail surface and the direction of normal microzone fluctuation, judge whether the optical response of adjacent area has common change trend, determine the spatial connectivity and diffusion trend of defect, and growing defect area;System executes the above method steps by processor.

Description

TECHNICAL FIELD

[0001] The present application relates to the detection of defects on the surface of a rail, and in particular to a method and system for detecting defects on the surface of a rail. BACKGROUND

[0002] At present, the prior art such as Chinese patent CN116645371A, a steel rail surface defect detection method and system based on feature search, although it has been systematically designed in terms of feature extraction, convolutional network fusion, multi-scale receptive field construction, etc., and has good end-to-end detection capability, but still has obvious technical limitations and deficiencies in practical application. First, this scheme mainly relies on the convolutional feature extraction of gray-scale images and depth maps, constructs a multi-scale feature fusion to generate a disease area heat map, and then performs secondary filtering through a depth map to determine the defect area. Although it can effectively detect macroscopic defects such as rail surface spalling, chunking, and cracking, it is difficult to ensure high-precision identification for small or hidden defects such as microscopic cracks, grain distortion, and early fatigue cracks on the rail surface, as the feature extraction error is easily caused by surface texture, light interference, or pollution. Since this method is completely based on the convolution of image features (multi-layer ResNet50 stacking + MRF multi-scale receptive field), it is extremely sensitive to texture-like pseudo-defects, especially under the influence of water stains, oil stains, or local residual images, the disease heat map is easily contaminated, and the subsequent depth Figure Two Although the secondary filtering can filter out some false defects with small height differences, it lacks an effective mechanism to distinguish pseudo-defects with similar optical properties (such as water film reflection and oil film scattering), and is prone to false positives or missed detection.

[0003] Secondly, this scheme highly depends on depth map, but in actual railway scene, track surface high reflection, light spot, pollution, oil stain can cause depth sensor failure or error, and further affect the determination of disease area. The limitation of depth map leads to insufficient detection accuracy of small scale erosion and micro cracks. In addition, the detection logic of the invention mainly focuses on feature processing of single frame image, although it is called end to end, but it does not fully utilize the time sequence information, and cannot dynamically perceive the evolution trend or cumulative fatigue expansion of defects, cannot judge whether the crack is static residue or expanding evolution, lacks time sequence stability analysis and dynamic evolution monitoring capability. Thirdly, this method does not introduce polarization detection and multispectral technology, and has no perception to optical disturbance under the condition of surface wet and slippery. In the face of rainy day inspection, water film covering and track surface lubricating oil residue, it is easy to identify flowing water film reflection, high light or oil stain as erosion or crack, and cannot effectively distinguish dynamic environmental disturbance from real disease. In addition, the existing technology has poor environmental adaptability, and the defect features are too dependent on the distribution of training data of the model. When the on-site environment is different from the training data (such as different reflection, pollution and wear path), the model performance is easy to degrade, and the generalization ability is limited. The locality of convolution network features leads to insufficient boundary detail processing capability, especially for crack endpoints and spatial derivative risk of small pieces, which cannot realize trend prediction. Furthermore, although this method expands the perception range through multi-scale receptive field, the layering of receptive field leads to large model calculation, although it claims to process a single 2048x4096 image at a speed of 100ms, but in the actual large-scale track full-range high-frequency inspection scene, there is still a speed bottleneck, especially in the continuous sampling dynamic scene, which cannot realize real-time online dynamic monitoring.

[0004] Finally, the method of the patent mainly relies on two-stage filtering of feature heat map and depth map, although it improves the robustness to a certain extent, but does not construct dynamic threshold regulation and growing area expansion mechanism, it is difficult to determine the spatial connectivity and diffusion priority path of crack in real time, cannot effectively evaluate the future development trend of crack, and lacks the ability to actively intervene in risk crack. In system integration, this method mainly faces static image reasoning, and does not perform joint perception and decision for multi-modal data (such as polarization, multispectral and time dynamic disturbance) under complex track surface state, the system design has single data dependence, which limits the environmental adaptability in engineering deployment.

[0005] In summary, the existing steel rail surface defect detection method based on feature search has certain technical advantages in feature fusion and multi-scale perception, but has many disadvantages such as insufficient micro crack recognition ability, poor environmental robustness, lack of dynamic evolution trend monitoring, limited pseudo defect distinguishing ability, strong dependence on depth map, lack of boundary derivative recognition, lack of time sequence perception, and inability to realize defect automatic growth and spatial expansion prediction. SUMMARY

[0006] The purpose of the present application is to provide a rail surface defect detection method and system, so as to solve some of the problems and deficiencies pointed out in the background art.

[0007] The present application solves the above-mentioned technical problems by adopting the following technical solution: a rail surface defect detection method, comprising: collecting a reflection response sequence of the rail surface under multiple illumination conditions by using dynamic disturbance with controllable incident angle and illumination intensity at different positions on the rail surface, to represent the micro-optical response behavior of the rail surface material; based on the collected reflection response sequence, calculating the dynamic reflection stability for each micro area of the rail surface, and marking the area with abnormal stability as a defect candidate area through the difference in area stability;

[0008] Analyzing the difference between the defect candidate area and the normal wheel-rail contact trace, combining the optical heterogeneity boundary feature, identifying whether there is abnormal residue or non-normal wear boundary caused by defect evolution, to verify the authenticity of the defect;

[0009] For the determined defect candidate area, judging whether the optical response of the adjacent area has a common change trend along the contact track direction and the normal micro area fluctuation direction of the rail surface, determining the spatial connectivity and diffusion trend of the defect, and growing the defect area.

[0010] Further, in the dynamic disturbance collection process, the polarization direction of the incident light is modulated to capture the reflection polarization abnormality caused by surface micro-cracks or grain distortion under the same incident angle and light intensity change conditions; during the reflection response sequence collection, different waveband multispectral light sources are used to distinguish between surface defects and potential damage inside the material.

[0011] Further, the calculation of the dynamic reflection stability includes multi-time period sliding window analysis on the reflection response sequence to identify the fluctuation trend of the micro area at different time scales, and to distinguish between short-term fluctuations caused by external environmental changes and persistent stability abnormalities caused by material damage; the stability abnormality determination combines the spatial autocorrelation of the reflection response, and uses the stability correlation degree between the micro area and the surrounding area to eliminate false abnormal markers caused by local shielding or contamination.

[0012] Further, the difference analysis between the defect candidate area and the normal wheel-rail contact trace uses bidirectional scanning sampling to judge whether the optical response has asymmetric changes under different contact directions, to identify abnormal wear boundaries caused by defect evolution; the analysis of the optical heterogeneity boundary feature includes boundary continuity detection of the local reflection gradient of the defect candidate area to judge whether there is boundary disturbance caused by foreign matter attachment or peeling.

[0013] Further, the identification of abnormal residues combines the response delay characteristics. Under dynamic light disturbance, it detects whether there is an optical response time lag phenomenon in the micro area to distinguish the surface residue from the material body reflection difference; in the diffusion analysis of the defect area along the contact track direction, combined with the historical data of the track wear direction, the priority path relationship of defect diffusion is established to preferentially judge the defect connectivity trend along the material mechanical fatigue direction.

[0014] Further, the diffusion judgment of the normal micro area fluctuation direction includes fluctuation synchrony analysis of the micro area response of the upstream and downstream of the defect candidate area, which is used to detect the spatial derivation risk of crack end point or micro block edge; the defect growth process adopts a dynamic correlation threshold strategy, which dynamically adjusts the growth boundary based on the stability change rate of the candidate area.

[0015] Further, in the defect candidate area verification process, the optical reflection change characteristics of the surface wet slip state are combined to identify the water film coverage or oil stain state, and to distinguish environmental interference from real defect characteristics; the time sequence stability fluctuation characteristics of the defect candidate area are used to determine whether there is an accumulated fatigue crack evolution, and the crack propagation direction is inferred through the dynamic fluctuation trend;

[0016] In the process of verifying the defect candidate area, the optical characteristic parameters of the surface state change are introduced, and a dynamic fluctuation function is constructed based on the response time sequence fluctuation behavior to identify the difference between real crack characteristics and environmental interference; in order to distinguish the real crack evolution characteristics of the rail surface from the pseudo defect signals caused by water film and oil stain factors, a response stability variation function integral model is adopted, which is defined as follows:

[0017] ;

[0018] Wherein:

[0019] represents the cumulative dynamic fluctuation index of the reflection response at time , incident angle , which is used to describe the defect evolution intensity; is the unit reflection intensity response at time and angle ; is the second order time derivative of the reflection response, which reflects the acceleration change of the reflection signal, i.e. the fluctuation intensity; is the response delay offset factor related to the incident angle, which is used to adjust the non-uniformity of light propagation caused by wet slip state; is the time decay background function, which is used to describe the slow change trend of water film or oil stain on the overall reflection, and has low frequency stability characteristics; It is a dynamic weighting function that reflects the weight priority at a specific angle and time, and is used to emphasize the anomalous enhancement region near the critical reflection angle; This is the sampling start time;

[0020] when Continued in multiple Exceeding the dynamic threshold in the direction At that time, it was determined that there was a real cumulative fatigue crack evolution behavior in the region, and if If the response exhibits periodic decay over time or is not concentrated in angular direction, it can be presumed to be a pseudo-response of optical interference caused by water film flow or oil contamination.

[0021] Furthermore, the optical reflection change characteristics of the wet and slippery surface include detecting the critical reflection angle response of the incident light, and identifying whether there is a water film or oil film adhering to the surface when the critical reflection angle shifts; the distinction of environmental interference includes comparing the reflection attenuation rate of surface micro-regions, analyzing the time decay mode of reflection intensity in continuous disturbance sampling, and distinguishing between static defects and pseudo-abnormal responses caused by dynamic water film flow.

[0022] Furthermore, the identification of environmental interference utilizes multispectral contrast response to determine changes in short-wave infrared absorption characteristics under water film conditions; the determination of oil pollution conditions combines changes in the polarization state of reflected light, utilizing the phenomenon of reduced polarization rate caused by surface oil film to distinguish oil pollution interference from the defective reflection characteristics of the rail surface itself.

[0023] A rail surface defect detection system, comprising:

[0024] The dynamic light disturbance module is used to project a light beam with adjustable incident angle, light intensity, polarization direction, and multispectral band onto the rail surface; the reflection acquisition unit is used to acquire the reflection signal from the rail surface under different lighting conditions.

[0025] The data processing unit, consisting of a processor and a memory, is used to process the reflected signal:

[0026] 1) Calculate the reflection stability of each micro-region and mark the areas with abnormal stability;

[0027] 2) Compare with normal wheel-rail contact marks to verify whether the abnormal area is a real defect;

[0028] 3) Analyze the spatial connectivity and diffusion trend of defects;

[0029] 4) Eliminate environmental interference from water film and oil film by utilizing critical reflection angle shift, shortwave infrared absorption variation, and reflection polarization rate variation;

[0030] An output unit is configured to display the defect position, diffusion trend and risk warning information; and the processor is programmed to perform the steps of the method.

[0031] The present application has the following advantages: by jointly collecting dynamic light disturbance, multi-spectral response and polarization information, the micro-optical behavior of the rail surface and near-surface layer can be comprehensively perceived, early defects such as micro-cracks, grain distortion and peeling can be effectively identified, and the shortcomings of traditional single-vision or profile collection means in easily missing fine defects can be made up, especially in complex environmental conditions, high stability and high sensitivity detection can still be maintained. By introducing critical reflection angle offset detection, multi-spectral reflection contrast, reflection attenuation rate analysis, polarization rate change perception and other multi-dimensional features, dynamic environmental interference such as water film, oil stain and dust attachment and real defect response can be effectively distinguished, false positives and missed detections caused by environmental factors can be reduced, and the adaptability of the system in actual railway scenes can be greatly improved. By using a time sequence sliding window and a dynamic fluctuation integral function, the cumulative evolution process of the crack can be monitored, the growth trend of the defect can be dynamically determined, early warning of fatigue crack propagation, peeling boundary evolution and other problems can be supported, the transition from static detection to dynamic monitoring can be realized, the operation and maintenance department can be helped to intervene in the early stage of defect development, and the accident risk can be reduced.

[0032] By using synchronous analysis of the contact trajectory direction and the normal fluctuation direction, combined with dynamic correlation threshold adjustment, the spatial connectivity determination and expansion determination of the defect area can be realized, the main expansion direction and boundary range of the crack can be automatically identified, the artificial judgment error can be reduced, and the post-processing workload can be reduced. By using joint intelligent discrimination of environmental perception, time sequence evolution and spatial growth, the dependence on traditional artificial secondary confirmation can be reduced, the inspection cost and manpower investment can be reduced, the detection efficiency and data reliability can be improved, and the scheme is suitable for high-frequency and long-distance rail inspection tasks. The scheme can be compatible with existing rail inspection vehicles, laser scanning equipment or vision systems, is easy to integrate and deploy, has good system engineering landing performance, can be applied to online inspection, rail inspection vehicles, unmanned aerial vehicle inspection and other scenes, and supports all-weather and all-scene automatic defect detection. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 An overall flowchart of the rail surface defect detection of the present application.

[0034] Figure 2 A functional relationship diagram of the rail surface multi-parameter defect detection of the present application.

[0035] Figure 3 A defect and environmental interference discrimination flowchart of the present application.

[0036] Figure 4 A simplified flowchart of rail crack multi-modal detection and verification of embodiment 1 of the present application.

[0037] Figure 5 Figure 2 is a simplified flowchart of the rail multi-angle spectrum detection and interference discrimination of the embodiment 2 of the present application. DETAILED DESCRIPTION

[0038] The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0039] In combination with the above Figure 1 The present application provides a rail surface defect detection method, which realizes high-precision and dynamic adaptive detection of rail surface defects. By applying dynamic disturbance with controllable incident angle and illumination intensity at different positions on the rail surface, multi-angle and multi-intensity illumination sampling of the rail surface is realized. The dynamic disturbance includes changing the incident angle of the illumination light source and adjusting the illumination level of the light source, so that different reflection responses are generated at the same surface position under different illumination conditions, and then the micro-optical behavior data of the surface material are constructed. Different angles of projection can be realized by using multiple light sources, and the light source intensity is adjusted synchronously by using an electronic controller. The illumination disturbance collection is carried out in a dynamic cycle during the track inspection process. Subsequently, based on the obtained multi-angle and multi-intensity optical reflection data, a reflection response sequence of different micro regions on the rail surface is established. The reflection response sequence of each micro region records the optical behavior of the micro region under dynamic disturbance conditions, and can truly reflect the surface state and micro characteristics of the region material. Then, dynamic reflection stability calculation is performed on the reflection response sequence. The so-called dynamic reflection stability refers to whether the optical reflection value of a micro region on the rail surface is stable, regular, or has abnormal fluctuations under the change of multiple illumination conditions. The calculation method includes: statistical analysis of the reflection response sequence of each micro region, calculation of the light intensity fluctuation amplitude, response gradient change, time sequence correlation coefficient and other indicators of each sampling point, and generation of the reflection stability value of the region. For a perfect rail surface, it usually has a relatively stable reflection response sequence, and its fluctuation characteristics show continuity and consistency. For regions with defects (such as cracks, corrosion, and block shedding), the reflection response will produce obvious fluctuation abnormalities under dynamic light disturbance due to changes in micro topography or material characteristics. By comparing the reflection stability of each micro region, using the regional stability difference analysis method, the regions with significant deviation from the surrounding regions are screened out, and these regions with stability abnormalities are marked as defect candidate regions. The stability abnormality judgment can be based on the dynamic threshold of the global stability distribution, or can be adaptively determined in combination with the neighborhood correlation, so as to effectively avoid the influence of environmental noise or local light and shadow interference, and ensure the accuracy and robustness of defect detection.

[0040] For the defect candidate regions marked by dynamic light perturbation and reflection stability analysis in the early stage, further defect authenticity verification is carried out. For each defect candidate region, the optical characteristics of the normal wheel-rail contact area are collected and compared. Due to long-term wheel-rail contact of the rail surface, the perfect rail surface will form regular contact wear marks, which show relatively consistent directionality, wear width and reflection stability in optical reflection, and usually have continuous and smooth reflection characteristics. Defects such as fatigue cracks, spalling, and corrosion, due to the destruction of the material structure or the mutation of the surface topography, will cause obvious abnormalities in the local reflection characteristics. In order to distinguish whether this abnormality belongs to a real defect or an accidental interference or environmental factor, the analysis of optical heterogeneity boundary characteristics is combined in the implementation process. A local neighborhood window is established around the defect candidate region to detect the optical boundary difference between the region and the surrounding intact area. The so-called optical heterogeneity boundary refers to the mutation degree of the reflection response of the candidate region and the normal region in space under the condition of dynamic light perturbation, mainly in the change of reflection intensity gradient, the discontinuity of spectral response, and the alienation of surface scattering mode. By calculating the reflection gradient distribution at the boundary of the candidate region, it is judged whether the boundary presents the non-continuous boundary characteristics formed by non-natural wear. For example, the normal wheel-rail contact wear boundary usually presents a regular gradient transition, while the defect-induced boundary presents local mutation, random fracture, and disordered reflection direction. Further, in order to verify whether there are abnormal residues or non-normal wear boundaries caused by defect evolution, the implementation combines multi-angle scanning of reflection time series to collect the response of the defect candidate region under multiple light states, and by analyzing the delay phenomenon and saturation state of the reflection response, it is identified whether there are abnormal residues such as oil stains, dust, metal chips, etc. on the surface. Since these residues are usually attached to the micro concave, crack or edge corrosion of the defect area, their optical reflection characteristics are different from those of smooth surfaces, showing phenomena such as increased light scattering, reflection saturation, and local light intensity abnormal change.

[0041] For the defect candidate area marked and verified by dynamic light disturbance and reflection stability analysis in the early stage, further spatial connectivity and diffusion trend judgment of the defect is carried out. For each determined defect candidate area, based on the actual operation of the rail surface physical structure, a two-way analysis framework of contact track direction and normal micro area fluctuation direction is established, wherein the contact track direction refers to the running track direction formed by the long-term rolling of the train wheel on the rail surface, which usually extends along the length direction of the rail, and the normal micro area fluctuation direction refers to the change direction of the local ups and downs perpendicular to the track contact surface, which is used to describe the diffusion or boundary extension of the defect in the surface normal direction. In the implementation process, first, an extension window is established in the edge of the defect candidate area in the contact track direction and the normal fluctuation direction respectively, and the optical reflection response data of the adjacent area is collected, and the reflection stability, light intensity fluctuation amplitude, response time series change trend and other indexes of the adjacent area under the condition of dynamic light disturbance are monitored. Then, the optical response data of the adjacent area is compared with the determined defect area, and whether there is a common change trend is analyzed, that is, the synchronism change of the optical response of multiple adjacent micro areas in dynamic sampling, such as overall decrease of reflection intensity, weakening of optical stability consistency, consistent abnormality of spectral reflection boundary, etc. If the adjacent area and the defect candidate area show similar abnormal response mode under dynamic disturbance, it is judged that these areas belong to the spatial extension part of the same defect structure. Further, in order to accurately judge the spatial connectivity of the defect, the correlation degree calculation between micro areas is adopted in the implementation process, and the response cooperative change degree of each adjacent micro area is quantified as a correlation coefficient. If the correlation coefficient exceeds the preset threshold, it is judged that the area and the defect candidate area exist physical defect connection and belong to the diffusion area of the defect. Through continuous analysis in the contact track direction, the extension trend of the crack along the running direction can be recognized, and combined with the normal micro area fluctuation analysis, the outward expansion behavior of the block, erosion or wear edge can be found, so as to realize the dynamic judgment of the spatial growth of the defect. The method gradually expands the defect boundary through the growth mechanism of the defect area, ensures the complete identification of the overall situation of the actual defect, and avoids the defect detection truncation or omission caused by the limitation of initial marking. Finally, the defect area after spatial diffusion judgment is uniformly marked as the extended defect connected area, the spatial form and diffusion trend of the defect are output, the dynamic monitoring ability for complex defects such as crack propagation, surface erosion and growth type wear is formed, and the method is suitable for omnidirectional intelligent detection and trend warning of rail surface defects.

[0042] Combination with the attached Figure 2, in order to further enhance the detection ability of the small cracks on the surface of the rail, grain distortion and potential damage inside the material, in the dynamic disturbance collection process, the joint technical scheme of incident light polarization direction modulation and multi-spectral response collection is adopted. On the basis of dynamic light disturbance sampling, by modulating the polarization direction of the incident light, the sensitive capture of different optical response characteristics of the rail surface is realized. Polarization modulation includes the alternative switching of linear polarization and circular polarization, and under the conditions of fixed incident angle and light intensity, by adjusting the polarization direction, the optical reflection characteristics of the surface microstructure are more revealed. Due to the existence of micro-cracks, grain distortion, metal organization unevenness and other phenomena on the surface of the rail, these local defects will disturb the polarization state of light, causing the polarization direction of reflected light to shift or the polarization degree to decrease, therefore, in the implementation, the polarization sensing device is used to measure the polarization state of the reflected light, and the polarization response difference between the defect area and the normal area under different polarization incident conditions is recorded, especially for the surface micro-cracks, due to their anisotropic characteristics, the change of reflected polarization will be more sensitive, and this characteristic can effectively distinguish the normal wear reflection from the polarization anomaly caused by cracks. Secondly, in the process of collecting the reflection response sequence, combined with the synchronous irradiation of multi-band multi-spectral light source, specific including the multi-source switching sampling of visible light band, near-infrared band and even short-wave infrared band, different bands of light have different penetration ability for the surface and the inside of the material, surface defects such as cracks and denudation mainly affect the reflection characteristics of visible light band, while potential damage inside the material, such as micro fatigue cracks and organization degradation, leads to the change of near-infrared and short-wave infrared reflection. Therefore, in the implementation process, the reflection response sequence under each band is recorded respectively, the surface reflection anomaly and the internal scattering change are compared, through the joint analysis of multi-spectral response, a multi-dimensional recognition model of defects is established, and the differentiation and recognition of the micro-cracks on the surface of the rail and the potential damage inside the material are realized.

[0043] In order to distinguish the real defects caused by material damage from the pseudo anomalies caused by environmental factors or interference more accurately, a method based on dynamic reflection stability is proposed. In the process of dynamic light disturbance sampling on the surface of the rail, the reflection response sequence of the same micro area under multiple light states is collected to form a time series reflection data set, which is input into the sliding time window for stability analysis. The sliding window includes multiple time scales, such as short window, medium window and long window, which are used to capture short-term environmental fluctuations and long-term material response changes. First, in the short window, the fluctuation amplitude, change rate and gradient of the reflection response are analyzed to determine whether there is a short-term abnormal fluctuation caused by light disturbance, temporary environmental factors such as dust, temporary obstacles, water stains and other factors. Then, in the long window, the cumulative fluctuation trend of the reflection response is detected to identify whether there is a stable and continuous decline or gradual amplification of the fluctuation mode. This long-term degradation of stability is usually related to material damage or surface fatigue crack propagation. By comparing the response characteristics of different time scales, the short-term accidental disturbance and material damage can be distinguished. Further, in order to avoid the misjudgment of isolated abnormality caused by local environmental interference, spatial autocorrelation analysis is introduced in the stability anomaly judgment. Specifically, the reflection stability of the target micro area and its adjacent areas is calculated, the dynamic reflection data of the adjacent areas is extracted by using the spatial sliding window, and the stability change trend of the target area and the surrounding area is compared. If the abnormal fluctuation of the target area has continuity or neighborhood synchronous change in space, it is determined that the possibility of real defect is high. On the contrary, if the fluctuation anomaly of the target area is irrelevant to the surrounding area, and the stability of the adjacent area is normal, it can be judged that the anomaly is caused by local pollution, attachment, light spot reflection and other factors, which belongs to pseudo anomaly. This spatial autocorrelation analysis quantifies the spatial logic of local anomaly by calculating the spatial correlation coefficient, fluctuation consistency index or stability gradient distribution, which significantly improves the accuracy of anomaly detection.

[0044] In order to further improve the identification accuracy of defect candidate regions and reduce the misjudgment rate, a technical solution combining bidirectional scanning sampling based on normal wheel-rail contact mark difference and optical heterogeneity boundary feature analysis is adopted. For each marked defect candidate region, optical response capture is performed using bidirectional scanning sampling. The so-called bidirectional scanning refers to dynamic light disturbance collection along the train running direction (forward direction) and the direction opposite to the running direction (reverse direction) for the same track surface region. The optical reflection response of the defect candidate region under different scanning directions is recorded, aiming to detect the influence of contact direction change on the reflection characteristics. Since the normal wheel-rail contact wear on the track surface has directionality, the optical reflection of the normal wear area shows symmetry or regular change under different scanning directions, reflecting the uniformity and predictability of the contact wear. However, if there is an abnormal wear boundary caused by defect evolution, such as erosion, spalling, crack propagation, etc., it will lead to non-symmetrical change of optical response in bidirectional scanning, for example, sudden change of light intensity in forward scanning and slow change in reverse scanning, or local reflection enhancement in one direction and reflection weakening in the other direction. This non-symmetrical feature can be used as an important basis for identifying abnormal wear boundaries. Further, in the optical heterogeneity boundary feature analysis, for the above detected non-symmetrical response area, local reflection gradient continuity detection is implemented, which specifically includes spatial sampling of the light intensity gradient at the boundary of the defect candidate region and analyzing the smoothness and continuity of the gradient change. The normal wheel-rail wear boundary usually shows a stable gradual gradient, reflecting the natural transition of material wear, while the boundary caused by foreign matter attachment or local spalling usually shows a sudden gradient, a jump in reflection intensity or a local extreme value. This boundary disturbance phenomenon can be quantified by the discontinuity index of the local gradient curve, such as calculating the number of sudden points of the first derivative or the extreme values of the second derivative. By comprehensively judging the optical reflection asymmetry under bidirectional scanning and the continuity feature of the boundary gradient, the real abnormal wear boundary caused by material defects can be effectively distinguished from the surface optical abnormalities caused by attachments, contaminants and spalling, further verifying the authenticity of the defects and the boundary range, and avoiding misjudgment and missed detection.

[0045] In order to effectively distinguish the surface residues from the actual defects of the material body, and improve the spatial connectivity judgment accuracy of defect detection, a technical scheme based on optical response delay characteristics is adopted to identify abnormal residues and analyze the defect diffusion priority path based on the historical data of the rail wear direction. In the dynamic light disturbance sampling process, the reflection response sequence of the candidate area and its neighborhood area of the rail surface defect is collected under different light conditions, and the optical response time lag phenomenon of the micro area is detected in the response sequence. The response delay refers to the fact that when the light intensity changes or the incident angle is disturbed, the optical reflection of the normal rail surface will immediately change, showing high response synchronicity. If there are abnormal residues such as oil stains, dust, water film and other residues on the surface, these attached layers will cause the reflected light to appear delayed response when the disturbance changes, that is, the response curve of light intensity change lags behind the time of the disturbance control signal. By comparing the synchronicity and delay curve of the optical response, it can be accurately distinguished whether the abnormal reflection behavior is caused by the material body or the surface residues. In addition, in order to further analyze the spatial connectivity and diffusion trend of the defect, the priority path relationship of the defect diffusion is established along the rail contact track direction of the defect area combined with the historical wear data of the rail. The historical wear data of the rail is analyzed, the fatigue stress concentration area and wear deviation trend of the rail are extracted, and a fatigue direction priority model is established. The model is based on the long-term stress characteristics of the rail to associate the fatigue direction of the material mechanics with the deviation of the track direction, and then deduce the extension tendency of the crack or block defect. In implementation, the micro area dynamic optical response cooperative analysis is carried out along the historical wear main path direction at the edge of the determined defect candidate area, to judge whether there is abnormal synchronous response in the adjacent micro area. If the adjacent micro area along the fatigue direction shows the continuity or tendency of the optical abnormality, these areas are further included in the defect connectivity area, and the spatial growth and expansion judgment of the defect is realized.

[0046] In order to accurately determine the spatial diffusion trend of rail surface defects, especially the growth behavior of crack endpoints or micro spalling edges, a joint determination mechanism of normal micro-area fluctuation synchronism analysis and dynamic correlation threshold adjustment is proposed. In the marked defect candidate area, the normal micro-area fluctuation direction is selected as the analysis axis. The normal micro-area fluctuation direction is perpendicular to the local high and low fluctuation change direction of the rail contact surface, which is orthogonal to the contact trajectory direction of the rail surface. Through the collection of dynamic optical reflection response of upstream and downstream (i.e. normal adjacent area) of the candidate area, the micro-area time sequence fluctuation curve is established. In the implementation process, the collected normal micro-area reflection response is analyzed in time sequence, the reflection intensity change, fluctuation amplitude and stability decay trend of the candidate area and its upstream and downstream adjacent micro-area are compared, and whether these areas exist response synchronism is judged. If it is found that the adjacent micro-area shows similar optical abnormal synchronous fluctuation under the dynamic light disturbance, especially the local fluctuation of the crack endpoint or the spalling edge is intensified, it is indicated that there is a spatial derivation risk, i.e. the defect continues to expand along the normal direction. In order to avoid the over-expansion of the defect growth boundary caused by environmental disturbance or data noise, a dynamic correlation threshold strategy is further adopted to control the defect growth process. According to the dynamic reflection stability change rate of the candidate area, the determination threshold of the defect growth is adjusted in real time. If the stability change rate is large, it indicates that the defect area appears rapid fluctuation anomaly in a short time, and the system reduces the growth threshold to allow the defect boundary to expand moderately, so as to capture the potential rapid evolution risk; otherwise, if the stability change rate is small, it indicates that the defect area is in a relatively stable or slow development stage, and the system increases the growth threshold to inhibit the overgrowth of the boundary and prevent the non-defect area from being mistakenly integrated into the defect connected area.

[0047] Combined with the Figure 3 In order to accurately distinguish the real crack evolution characteristics of the rail surface from the optical pseudo-abnormalities caused by environmental factors (such as water film covering or oil stain adhesion), a defect candidate area verification mechanism based on dynamic optical disturbance sampling is proposed, which uses the time sequence stability fluctuation characteristics combined with the optical response difference of the surface wet and slippery state for comprehensive judgment. In the defect candidate area verification process, multi-angle and multi-time reflection response data are collected, and the surface wet and slippery state is modeled as a key interference factor. Under the wet and slippery state, the reflection light intensity change of the rail surface usually shows overall slow decay or response delay, which is easy to be confused with the reflection abnormality of real defects such as micro-cracks and fatigue erosion. Therefore, the optical characteristic parameters of surface state change are introduced, and the dynamic fluctuation function is constructed based on the collected multi-time data to identify the cumulative evolution behavior of real defects. Specifically, the reflection data is processed by using the response stability variation function integral model, which is defined as follows:

[0048] ;

[0049] Where, represents the time at which the incident angle , the cumulative dynamic fluctuation index of the reflection response, used to characterize the intensity of defect evolution; is the time and the angle unit reflection intensity response; is the second-order time derivative of the reflection response, reflecting the acceleration change of the reflection signal, embodying the severity of fluctuation; is the response delay offset factor related to the incident angle, used to adjust the light propagation path offset caused by water film or oil film, making the model adapt to the wet and slippery state response characteristics of different angles; is the time decay background function, used to represent the overall optical reflection slow change caused by the flow of water film or oil stain on the surface, usually with low-frequency stability; is the dynamic weight function, used to weight the sensitivity of reflection fluctuation at a specific angle and time, especially near the critical reflection angle, which can be used to emphasize local reflection abnormal change; is the sampling start time. Through the above model, the high-frequency local response fluctuation caused by real defects can be effectively distinguished from the low-frequency overall reflection trend caused by water film and oil stain. When the fluctuation exceeds the dynamic threshold in multiple different incident angle directions for a long time, it is determined that there is a real cumulative fatigue crack evolution behavior in this area, indicating that the material shows consistent fluctuation intensification phenomenon under multi-angle disturbance, with typical defect expansion characteristics; while when it presents periodic decay in time or is not concentrated in the distribution of each angle direction, showing randomness or flow characteristics, it is inferred that the optical interference false response is caused by water film flow or oil stain adhesion, thus effectively avoiding false positives.

[0050] In order to effectively distinguish the real defects on the surface of the rail from the optical interference caused by environmental factors (such as water film, oil film), a multi-angle dynamic judgment mechanism based on the optical reflection characteristics of the surface wet and slippery state is proposed, which specifically includes the critical reflection angle response detection of incident light and the time series analysis of reflection decay rate. First, in the dynamic disturbance sampling process, by gradually adjusting the angle of incident light, the reflection intensity change of the rail surface under different incident angle conditions is detected, and the response characteristics of the critical reflection angle are particularly concerned. The so-called critical reflection angle is the specific angle at which the light from the air into the liquid layer occurs total reflection or reflection rate mutation when there is liquid coverage on the interface. Because the refractive index of water film or oil film is different from that of dry rail surface, when the surface is covered with liquid, the critical angle position will shift obviously. By comparing the response changes of the critical angle in different areas, it can be quickly judged whether there is water film or oil film attached. For example, in the air-metal interface, the reflection changes regularly with the change of incident angle, while in the air-liquid-metal multi-interface, the reflection rate will produce inflection point or shift at a specific angle. In implementation, the change is obtained by high-precision angle scanning to realize real-time identification of the wet and slippery state. Secondly, in the process of distinguishing environmental interference, the reflection decay rate of the surface micro area is further used for dynamic analysis. Specifically, in the continuous optical disturbance sampling, the change trend of the reflection intensity of each micro area with time is recorded, and the time series decay curve is constructed. For static defects such as cracks and blocks, the abnormal optical reflection shows stable reflection intensity mutation or fixed local dark area, and the decay rate is usually zero or stable. And for dynamic environmental factors such as water film and oil film, due to their fluidity or evaporation effect, the surface reflection intensity will gradually decay or fluctuate with time, showing a slow changing reflection decay mode. In specific implementation, the reflection intensity change rate of each micro area is calculated. If a sustained decay trend or periodic fluctuation is detected, and there is synchronous low-frequency change with the adjacent area, it can be judged as a pseudo abnormal response caused by environmental interference.

[0051] In order to accurately identify the false defect signals caused by environmental factors, especially the optical anomalies caused by water film and oil stain adhesion, a joint environmental interference identification method based on multispectral contrast response and polarization state analysis is proposed. In the dynamic optical sampling process, a multispectral light source is used to irradiate the rail surface, including visible light band, near-infrared band and short-wave infrared band, and the reflection signals of each band are collected and compared synchronously. Because the water film has obvious absorption characteristics in the short-wave infrared band, compared with the dry rail surface or solid defect area such as crack, the water film covered area will show significant attenuation of short-wave infrared reflection. By comparing the reflection intensity changes of the same surface area under different spectra, especially the abnormal absorption phenomenon in the short-wave infrared region, it can be quickly judged whether there is water film coverage. For example, in the visible light band and near-infrared band, the reflection changes of water film and small surface defects are similar, but in the short-wave infrared band, the absorption of water film to light leads to a significant decrease in reflection intensity, which can be used to effectively distinguish water film interference from real surface defects. In order to further distinguish the oil stain state from the reflection characteristics of the real defects of the rail surface, on the basis of multispectral sampling, combined with the dynamic analysis of the polarization state of the reflected light, specifically: the linearly polarized or circularly polarized light is incident on the rail surface, and the polarization ratio change of the reflected light is collected. Because the oil film adhesion will cause multiple scattering and interference of the surface light, causing the polarization ratio of the reflected light to decrease, the metal defects or wear areas of the surface body usually maintain a high polarization retention, especially at a specific incident angle, such as near the Brewster angle, the polarization characteristics of the metal reflection are more stable. When implemented, by measuring the polarization parameters of the reflected light, such as polarization degree, extinction ratio or polarization direction offset, it is judged whether there is an abnormal decrease in polarization ratio caused by oil stain, if the polarization degree is detected to decrease significantly and the spatial distribution overlaps with the abnormal area of visible light reflection, it can be inferred that the area is an oil film interference, rather than a crack or wear of the material body.

[0052] Embodiment 1:

[0053] The accompanying drawings Figure 4In this embodiment, a certain railway bureau is set to inspect a heavy-load freight rail section using a multi-modal detection device. The inspection equipment is equipped with a multi-spectral imaging module with polarization modulation function. When the inspection vehicle travels at a speed of 40 km / h, it dynamically samples the rail surface. During on-site detection, the inspection system finds a local reflection abnormal area in a certain section of the track. The system first uses visible light polarization modulation to control the incident light. The modulation method uses linear polarization 0°, 45°, and 90° switching in turn, and the light intensity remains fixed. The incident angle is set to 20° to exclude strong reflection interference. The polarization retention rate of the normal track surface reflection changes at different polarization angles, and the polarization reflection ratio fluctuates within ±3%. In the abnormal area, the polarization reflection ratio is 18% at 0° polarization, drops to 11% at 45°, and rises to 16% at 90°, with a fluctuation amplitude of more than 7%, which is significantly higher than the normal track surface polarization change range. After comparative analysis, it is preliminarily judged that there is a microstructure abnormality in this area, such as grain distortion or fine cracks. In addition, to further verify the type of the abnormal area, the system collects multi-spectral response of the same micro area, records the reflection response under visible light (wavelength 550 nm), near-infrared (wavelength 850 nm), and short-wave infrared (wavelength 1450 nm) three wavebands. The sampling results show that in the visible light band, the reflectivity of the abnormal area is 36%, which is similar to that of the surrounding normal area; in the near-infrared band, the reflectivity of the abnormal area decreases to 21%, which is 7% different from the 28% reflectivity of the normal area; in the short-wave infrared band, the reflectivity of the abnormal area further decreases to 12%, while the normal area remains at about 24%. According to the gradual attenuation characteristics of multi-spectral response, it can be inferred that the abnormal area not only has surface reflection abnormalities, but also has potential damage inside the material, such as grain distortion, metal fatigue layer, or subsurface micro-cracks. Because these internal defects will cause a significant decrease in infrared and short-wave infrared reflection, combined with the dual evidence of polarization abnormality and multi-band attenuation, the system finally determines that the area is a defect area with crack evolution risk. Further manual review using ultrasonic detection verification indeed detects a micro-crack initiation point in the area, with a crack length of about 4 mm and a depth of about 0.3 mm. The entire detection process starts from polarization dynamic disturbance collection, and through real-time optical response analysis combined with multi-spectral comparison, surface micro-cracks and potential internal damage of the material can be accurately distinguished, achieving precise identification of defect type and distribution, significantly reducing the influence of environmental interference, and improving the sensitivity of early crack identification.

[0054] After the preliminary detection based on polarization modulation and multi-spectral reflection response, the inspection team continued to conduct a more in-depth dynamic reflection stability analysis on the abnormal area to verify whether the crack was in a cumulative expansion state and to rule out the influence of environmental interference. The detection system implemented multi-time period sliding window analysis on the reflection response sequence of the area, setting short-time window (0.5 seconds), medium-time window (2 seconds) and long-time window (5 seconds) respectively. In the short-time window, the detection found that the reflection intensity had small fluctuations, with a fluctuation amplitude of ±2%, which was attributed to environmental interference such as sunspot or windblown dust. In the long-time window, the reflection intensity of the abnormal area continuously decreased, with an average decrease of 9%, accompanied by a gradual decrease in stability index, indicating the existence of persistent abnormalities caused by material structure changes.

[0055] To further rule out false abnormal signals, the system conducted a spatial autocorrelation analysis on the area, comparing the stability change trend of the abnormal micro-area with that of the adjacent 5 micro-areas. It was found that the stability fluctuation synchronization correlation coefficient of the adjacent areas was 0.92, indicating that the anomaly was not isolated, but had spatial consistency of stability degradation, further confirming the existence of the crack. Subsequently, the system used bidirectional scanning sampling to analyze the difference in the direction of wheel-rail contact of the abnormal area. The inspection vehicle implemented forward and reverse sampling on the same track section. When moving forward, the detection found that the reflection intensity of the area suddenly changed to 14%, while moving backward, the reflection intensity changed smoothly to 19%, with a difference of 5%, which was significantly higher than the average difference (only 1%) of the surrounding normal wear area. Combined with the analysis of the track mechanics model, it was judged that the difference was caused by the abnormal wear boundary, which meant that the crack or erosion area formed an irregular boundary reflection characteristic.

[0056] To further confirm the boundary state, the system extracts the local reflection gradient and finds that the reflection gradient of the boundary of this area changes from the normal 0.3% per millimeter to a sudden jump of 1.2% per millimeter of light intensity, indicating that the boundary has obvious optical heterogeneity, which is consistent with the boundary disturbance characteristics of material erosion or crack expansion. At the same time, the system also detects the influence of abnormal residues and uses the optical response time delay characteristics for dynamic analysis. Under continuous light disturbance, the response delay of the normal rail surface area is 0.02 seconds, while in the abnormal area, the local micro-area response delay reaches 0.11 seconds, with obvious lag. Combined with on-site observation, there is oil stain attachment in this area, but the reflection attenuation of the oil stain area shows a decrease in all wavebands without obvious boundary gradient change in multispectral sampling, while the crack area shows local polarization reflection anomaly and boundary light intensity mutation, so the system successfully distinguishes oil stain residues from the body defects through time delay characteristics. Subsequently, combined with track maintenance history data and fatigue path analysis, the system establishes a defect diffusion priority path model. Track wear data shows that the wheel-rail contact in this section is concentrated in the left center of the rail head, and long-term stress causes the crack to expand left along the contact trajectory. The system accordingly prioritizes scanning for defect connectivity along the fatigue direction and finds that the crack extends 7.5 mm along the contact trajectory direction, while the normal extension is only 2.1 mm, verifying the priority expansion trend of the fatigue main path.

[0057] In view of the derived risk of crack endpoints and micro-spalling edges, the system also implements normal micro-area wave fluctuation synchronization analysis to compare the stability fluctuation of the upstream and downstream areas of the crack. It is found that the fluctuation correlation coefficient of the adjacent areas in the normal direction reaches 0.87, indicating that there is a spatial derivation risk in the endpoint area. To prevent over-expansion of defect recognition, the system uses a dynamic correlation threshold strategy based on stability change rate to dynamically adjust the growth boundary. When the crack stability change rate exceeds 10%, the system reduces the growth threshold and expands the detection range, while when the edge area change rate is less than 3%, the system shrinks the growth boundary to avoid incorporating environmental noise into the defect area. Finally, the detection system outputs the spatial topology distribution map of the crack, recording the total length of the crack as 8.2 mm, with the main crack direction extending 7.5 mm along the track running direction and the normal extension being 2.1 mm. The crack depth is estimated to be 0.35 mm, and the system marks this area as a moderate risk defect and recommends rail grinding and crack sealing repair within 3 days.

[0058] Example 2:

[0059] In combination with the attached Figure 5After the inspection vehicle completed polarization modulation, multispectral identification, dynamic stability analysis, and spatial expansion determination, in order to further verify whether there was a cumulative fatigue crack evolution phenomenon in the crack area and to eliminate optical interference caused by residual water film after rain and oil contamination from track lubricating oil, technicians used a response stability variogram integral model to perform high-precision determination of the abnormal area. The detection period was from 9:10 to 9:11 on the same day, and a total of 298 sets of optical response sequence data were collected at three incident angles in this area ( , , The reflection response R(t,θ) is synchronously acquired and subjected to multi-time series dynamic fluctuation assessment.

[0060] This method is used in each Calculate the second time derivative of the reflection response:

[0061] ;

[0062] set up The sampling interval is 50ms. The change in reflection acceleration for each set of data can be obtained using the above formula. To eliminate delay errors caused by slippery conditions, the system introduces a [missing information - likely a parameter or parameter]. Related response delay factor In this test, based on the analysis of the reflection interference of the actual water film refractive index at different angles, the values ​​are as follows: , , At the same time, a low-frequency background function is introduced. To simulate the attenuation trend under water film flow, the function uses an approximate model as follows: The results were obtained from on-site fitting. , , used to represent the slow response of the slippery area.

[0063] The entire dynamic fluctuation function Defined as:

[0064] ;

[0065] in, It is a dynamic weighting function, constructed in this invention as a Gaussian distribution set for the critical reflection angle (approximately 39°), to emphasize the... Optical anomalies near the critical angle:

[0066] ;

[0067] Substitute the above parameters into the formula, to Taking the incident angle direction as an example, the calculation is performed over the time interval. The dynamic response fluctuation integral within the range, after numerical integration, yields... , and and 45° direction, respectively , Compare with the dynamic thresholds set by this system. The results show that: in more than two angular directions The region meets the identification criteria for the true evolution of fatigue cracks, and is therefore identified as being in a state of continuous crack propagation.

[0068] Conversely, the inspectors performed comparative calculations at another abnormally high reflectivity point approximately 8 meters away from the defect area. This area had a large water film on its surface due to lingering water from the previous night's rain. The calculations showed that at the same angle... The results were: 0.91 in the 15° direction, 1.03 in the 30° direction, and 0.94 in the 45° direction. Although there were occasional local disturbances, the overall value showed low amplitude and periodic fluctuations, and did not exceed [a certain value] in any direction. The spatial distribution is also not concentrated, so the system determines that the point is a pseudo-response of water film interference and does not constitute a structural defect.

[0069] Finally, after manual crack detection and verification, the first area had a fatigue initiation point and had extended to a length of 8.6 mm. The crack edge highly coincided with the outline drawn by the system detection, with an error of less than 1 mm. No cracks or damage were detected in the second area, only local surface moisture marks were found.

[0070] On-site inspections continued to delve into the assessment of interference from wet and slippery environments. Particularly noteworthy is the presence of residual water films and oil stains from vehicle lubricant drips along the railway line after rain. If these are not properly differentiated, they can easily lead to missed or false alarms regarding cracks. Therefore, critical reflection angle response detection and reflection attenuation rate analysis were conducted on areas suspected of environmental interference. Taking track section A-07 as an example, the inspection system collected reflection intensity variation curves from an incident angle of 0° to 60°. It was found that on a normal, dry track surface, the relationship between reflection intensity and incident angle showed a stable linear increase without a significant inflection point, with the maximum reflectivity reaching 40% at 45°. However, in abnormal areas, the inspection equipment recorded a sudden change in reflection intensity at 38°, dropping abruptly from 31% to 18%. This sudden change was identified as a critical reflection angle shift. Based on optical principles, when the rail surface is dry, the critical angle of the air-steel interface should be a high absorption state close to no total internal reflection. However, when the surface is covered with a water film, forming a multi-layer air-water-steel interface, the reflection characteristics will change. By reverse calculation using a reflection model, the critical angle corresponding to the field data decreases from the theoretical 39° to 38°, which is consistent with the characteristics of thin water film interference.

[0071] Further, to determine whether it is a dynamic water film interference, the inspection system implements continuous disturbance sampling on the micro area, sets the time window to 5 seconds, the sampling frequency to 20 Hz, and records the time decay curve of the reflection intensity. In the dry crack area, the reflection intensity fluctuates by only ±1% over time, while in the water film area, the reflection intensity continuously decays from the initial 30% to 22%, with an average decay rate of 1.6% per second. The following decay rate model is used on site for calculation:

[0072]

[0073]

[0074]

[0075] The rate is much higher than the normal track surface, indicating that the surface exists liquid flow caused optical decay, further verifying that it is a dynamic water film interference.

[0076] To avoid relying on a single angle for judgment, the system also introduces multi-spectral reflection data for auxiliary verification. In the same area, the reflection intensities at 550 nm (visible light), 850 nm (near-infrared), and 1450 nm (short-wave infrared) are collected. In the dry crack area, the reflectance changes at the three wavelengths are 36%, 29%, and 25%, respectively; while in the water film covered area, the visible light reflectance is 32%, the near-infrared decreases to 24%, and the short-wave infrared sharply drops to 11%. Based on the short-wave infrared absorption characteristics of water, it is confirmed that this significant decay conforms to the water film absorption model and does not conform to the crack reflection characteristics, so the area is finally determined to be a water film covered pseudo anomaly.

[0077] In addition, to distinguish oil pollution interference, the system uses polarization state analysis, uses polarized light sources for illumination, and records the polarization ratio of reflected light. The reflectance at polarization angles of 0° and 90° for the normal track surface is 34% and 36%, respectively, and the polarization preservation rate (PPR) is:

[0078]

[0079] In the oil pollution area, the polarization reflection collected is 29% and 30%, respectively, and the PPR is:

[0080]

[0081] The polarization ratio is significantly reduced, close to the non-polarized reflection state, which conforms to the scattering depolarization phenomenon caused by oil film. Combined with the site environment, it is found that this area is exactly located in the train track lubrication section, where there is lubricating oil residue. Through the reduction of polarization ratio, the system accurately determines it to be oil pollution interference rather than material defects. ​​​​​​​​

[0082] According to the above data, the final output inspection report classifies the reflection anomaly of the A-07 section, the water film interference area is 2.4 meters, the oil stain interference area is 0.8 meters, and the real crack detection area is an 8.6 mm crack belt, completely eliminating false positives caused by environmental factors, and ensuring high precision and high reliability of track defect detection.

[0083] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for detecting defects on the surface of railway tracks, characterized in that, include: By employing dynamic perturbations with controllable incident angles and light intensity at different locations on the rail surface, reflection response sequences of the rail surface under various lighting conditions are collected. These reflection response sequences are time-series reflection signal datasets collected under different lighting conditions, used to characterize the microscopic optical response behavior of the rail surface material. Based on the collected reflection response sequences, dynamic reflection stability is calculated for each micro-region on the rail surface. The dynamic reflection stability is calculated by statistically analyzing the reflection response sequences of each micro-region, calculating the light intensity fluctuation amplitude, response gradient change, and time series correlation coefficient of each sampling point, and generating the reflection stability value of the region. By the difference in regional stability, regions with abnormal stability are marked as candidate defect regions. The difference between the defect candidate area and the normal wheel-rail contact trace is analyzed. By performing bidirectional scanning sampling along the train running direction and in the reverse direction, the symmetry change of bidirectional optical reflection is compared. In the area where asymmetric response is detected, the continuity of local reflection gradient is detected. The abnormal area is comprehensively judged to determine whether it is an abnormal residue or abnormal wear boundary generated by defect evolution, so as to verify the authenticity of the defect. For the identified candidate defect regions, the optical response of adjacent regions is judged to have a common trend of change along the contact trajectory direction and the normal micro-area fluctuation direction on the rail surface. The contact trajectory direction is the wear trajectory direction along the rail length formed by the long-term operation of the train wheel on the rail surface, and the normal micro-area fluctuation direction is the change direction of local undulations perpendicular to the rail contact surface. It is used to describe the diffusion or boundary extension of the defect in the surface normal, thereby determining the spatial connectivity and diffusion trend of the defect, as well as the defect growth region.

2. The method for detecting defects on the surface of railway tracks according to claim 1, characterized in that, During the dynamic disturbance acquisition process, the polarization direction of the incident light is modulated to capture the reflection polarization anomaly caused by surface microcracks or grain distortion under the same incident angle and light intensity variation conditions. During the reflection response sequence acquisition, a multispectral light source with different wavelengths is used to distinguish between surface defects and potential damage inside the material.

3. The method for detecting defects on the surface of a railway track according to claim 2, characterized in that, The calculation of dynamic reflection stability includes performing multi-time sliding window analysis on the reflection response sequence to identify the fluctuation trend of the micro-region at different time scales and distinguish between short-term fluctuations caused by changes in the external environment and persistent stability anomalies caused by material damage. The determination of stability anomalies is combined with the spatial autocorrelation of the reflection response and the degree of stability correlation between the micro-region and the surrounding region to eliminate false anomaly markers caused by local shading or pollution.

4. The method for detecting defects on the surface of a railway track according to claim 3, characterized in that, The difference analysis between the defect candidate region and the normal wheel-rail contact trace is performed by bidirectional scanning sampling to determine whether there is an asymmetric change in the optical response under different contact directions, which is used to identify abnormal wear boundaries caused by defect evolution; the analysis of optical heterogeneity boundary characteristics includes boundary continuity detection of the local reflection gradient of the defect candidate region to determine whether there is boundary disturbance caused by foreign matter attachment or peeling.

5. The method for detecting defects on the surface of a railway track according to claim 4, characterized in that, The identification of abnormal residues, combined with response delay characteristics, detects whether there is an optical response time lag in the micro-area under dynamic light disturbance, in order to distinguish the difference between surface residues and material body reflection. In the diffusion analysis of the defect region along the contact trajectory, historical data of track wear direction are combined to establish the preferential path relationship of defect diffusion and prioritize the judgment of the defect connectivity trend along the material mechanical fatigue direction.

6. The method for detecting defects on the surface of a railway track according to claim 5, characterized in that, The diffusion determination of the normal micro-region fluctuation direction includes fluctuation synchronization analysis of the micro-region response upstream and downstream of the defect candidate region, which is used to detect the spatial derivative risk of crack endpoints or micro-chip edges; the defect growth process adopts a dynamic correlation threshold strategy, which dynamically adjusts the growth boundary based on the stability change rate of the candidate region.

7. The method for detecting defects on the surface of a railway track according to claim 1, characterized in that, During the verification of the defect candidate region, the optical reflection change characteristics of the wet and slippery surface are combined to identify the water film coverage or oil stains, and to distinguish between environmental interference and real defect features. By utilizing the temporal stability fluctuation characteristics of defect candidate regions, it is determined whether there is cumulative fatigue crack evolution, and the direction of crack propagation is inferred from the dynamic fluctuation trend.

8. A method for detecting defects on the surface of a railway track according to claim 7, characterized in that, The optical reflection change characteristics of the surface in a wet and slippery state include detecting the critical reflection angle response of incident light, and identifying whether there is a water film or oil film adhering to the surface when the critical reflection angle shifts; the distinction of environmental interference includes comparing the reflection attenuation rate of surface micro-regions, analyzing the time decay mode of reflection intensity in continuous disturbance sampling, and distinguishing between static defects and pseudo-abnormal responses caused by dynamic water film flow.

9. A method for detecting defects on the surface of a railway track according to claim 8, characterized in that, The identification of environmental interference is achieved by using multispectral contrast response to determine changes in short-wave infrared absorption characteristics under water film conditions; the determination of oil pollution conditions is combined with changes in the polarization state of reflected light, utilizing the phenomenon of reduced polarization rate caused by surface oil film to distinguish oil pollution interference from the defective reflection characteristics of the rail surface itself.

10. A rail surface defect detection system, characterized in that, include: The dynamic optical disturbance module is used to project a beam of light with adjustable incident angle, light intensity, polarization direction, and multispectral band onto the rail surface. The reflection acquisition unit is used to collect the reflection signals from the surface of the railway track under different lighting conditions; The data processing unit, consisting of a processor and a memory, is used to process the reflected signal: 1) Calculate the reflection stability of each micro-region and mark the areas with abnormal stability; 2) Compare with normal wheel-rail contact marks to verify whether the abnormal area is a real defect; 3) Analyze the spatial connectivity and diffusion trend of defects; 4) Eliminate environmental interference from water film and oil film by utilizing critical reflection angle shift, shortwave infrared absorption variation, and reflection polarization rate variation; An output unit is used to display defect location, spread trend, and risk warning information; the processor is programmed to perform the steps of the method according to any one of claims 1-9.

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