Image perception-based road de-icing material loss detection method

Abstract

This invention discloses a method for detecting the loss of slow-release anti-icing materials for roads based on image perception, belonging to the field of road engineering and road maintenance technology. The method includes lateral zoning of roads, zonal sampling, extraction and detection, and calculation and evaluation of de-icing agent loss in each zone. This invention solves the problems of existing evaluation methods failing to reflect lateral loss differences, having incomplete evaluation criteria, and lacking operability. It achieves zonal quantitative determination of anti-icing material loss, providing a reliable basis for performance evaluation, life prediction, and differentiated maintenance of anti-icing roads.

Description

Technical Field

[0001] This invention belongs to the field of road engineering and road maintenance technology, specifically relating to a method for detecting the loss of road slow-release anti-icing materials based on image perception. Background Technology

[0002] Anti-icing roads incorporate de-icing agents or anti-icing materials into the pavement structure layer beforehand. Under adverse weather conditions such as low temperatures and snowfall, these agents gradually release their active ingredients, lowering the icing threshold and reducing the frequency and amount of manual de-icing agent application, thus ensuring road safety. In actual service, anti-icing roads are affected by factors such as driving habits and lane markings, resulting in significant lateral concentration of vehicle trajectories. Vehicle tires repeatedly and continuously act on fixed wheel track areas, leading to fundamental differences in stress states, hydrodynamic erosion conditions, and wear behavior between wheel track areas and non-wheel track areas.

[0003] This difference in lateral stress directly leads to a non-uniform distribution of anti-icing material wear in the lateral direction of the road. The wheel track area, subjected to high-frequency, high-stress wheel loads, exhibits more pronounced wear, dissolution, and carry-over effects of the anti-icing material, with a wear rate far exceeding that of non-wheel track areas. However, current technologies for evaluating the performance of anti-icing road de-icing agents generally employ overall sampling or overall release testing methods, failing to consider the spatial non-uniformity of vehicle wheel loads and ignoring the wear differences between wheel track and non-wheel track areas. This results in test results that cannot accurately reflect the true service condition of anti-icing roads, making it difficult to provide reliable quantitative data for differentiated road maintenance, precise upkeep, and lifespan prediction.

[0004] Specifically, the existing technology has the following shortcomings: (1) One-sided evaluation method: The existing evaluation of the loss of anti-icing road de-icing agent is mostly characterized by the overall average loss, which cannot reflect the loss difference between different areas of the road in the transverse direction, and is prone to misjudgment of the anti-icing performance of the road surface. (2) Incomplete evaluation basis: The spatial non-uniformity of vehicle wheel load was not included in the evaluation system, and the objective fact that the de-icing agent in the wheel track area was consumed faster was ignored, resulting in a lack of specificity in the evaluation results; (3) Lack of operability: There is a lack of a repeatable and engineerable zoning measurement method, making it difficult to achieve spatial zoning quantification of de-icing agent loss and unable to provide effective quantitative analysis support for the life assessment and precise maintenance of anti-icing roads. Summary of the Invention

[0005] Technical problem solved: To address the above-mentioned technical problems, this invention provides a method for detecting the loss of road slow-release anti-icing materials based on image perception. This method solves the problems that existing evaluation methods cannot reflect lateral loss differences, have incomplete evaluation criteria, and lack operability. It enables the zonal quantitative determination of anti-icing material loss, providing a reliable basis for the performance evaluation, life prediction, and differentiated maintenance of anti-icing roads.

[0006] Technical solution: A method for detecting the loss of road slow-release anti-icing materials based on image perception, comprising the following steps: S1. Road Lateral Zoning: A lateral coordinate system is established with the road lane centerline as the lateral reference benchmark. The cumulative effect of vehicle tires on the road surface at different lateral positions is defined as the vehicle load influence intensity. This vehicle load influence intensity is used as the core basis for zoning, dividing the road cross section into the main wheel track zone, the extended wheel track zone, and the non-wheel track influence zone. The vehicle load influence intensity is characterized by the trajectory density of vehicle tires on the road lateral. The trajectory density reflects the concentration of vehicle tire passage within a unit lateral length. The trajectory density data is obtained in real time through sensor detection. The sensor can continuously capture, record, and statistically analyze the vehicle tire trajectories at different lateral positions on the road, accurately outputting the frequency of tire passage per unit length at each lateral point. This serves as the core data support for the trajectory density, thereby quantifying the vehicle load influence intensity and providing accurate and objective measurement basis for road lateral zoning. S2. Sampling, extraction and detection in zones: Obtain road surface samples with consistent unit area or unit volume in the three zones in step S1. After pretreatment of crushing, drying and weighing the samples, soak or rinse the samples in solvent to obtain snow melting agent extract. Then detect the content of characteristic ions representing the effective components of snow melting agent in the extract. S3. Calculation and evaluation of de-icing agent loss in each zone: Based on the characteristic ion detection results, combined with the sample mass, solvent volume, and conversion relationship between characteristic ions and effective components of de-icing agent, calculate the de-icing agent loss per unit area or unit volume in each zone, compare and analyze the differences in loss in each zone, and obtain the distribution characteristics of de-icing agent loss in the transverse direction of the road.

[0007] Preferably, the main wheel track zone is the core area that vehicle tires repeatedly pass through over a long period of time, and it is the area with the greatest impact from vehicle load. In this area, the number of times vehicle wheel loads are applied is the highest, the contact stress and shear stress on the road surface are the greatest, and the wear, dissolution, and carry-out effects of anti-icing materials are most significant. Based on the centerline of the vehicle wheel track, the lateral range of the main wheel track zone is set to extend 0.4-0.5m to the left and right.

[0008] Preferably, the extended wheel track zone is a secondary action area adjacent to the main wheel track zone, mainly formed by lateral movement, lane changes, and changes in driving status during vehicle movement. The frequency of vehicle wheel load action in this area is lower than in the main wheel track zone, but still significantly higher than in other areas of the road. The wear of the anti-icing material is mainly affected by hydrodynamic scouring and intermittent wheel load action. The extended wheel track zone is located outside the main wheel track zone, and its lateral range is defined as an area extending 0.8-1.1m to the left and right from the vehicle wheel track centerline. The boundary between the main wheel track zone and the extended wheel track zone is determined by the location where the vehicle tire track density detected by the sensor shows a significant decrease from the center to both sides. The boundary point is accurately defined using the track density data output by the sensor, ensuring the scientific accuracy of the zoning.

[0009] Preferably, the non-wheel track influence area is the road area other than the main wheel track area and the extended wheel track area. In this area, the direct effect of vehicle wheel load is minimal, and the loss of anti-icing material mainly comes from environmental factors, such as leaching from rainfall and natural aging. Its loss rate is significantly lower than that of the wheel track related area.

[0010] By using the hierarchical zoning based on the influence intensity and trajectory density of vehicle loads, the road surface samples within the same zoning are consistent in terms of stress state, wear mode, and anti-icing material loss mechanism. This avoids detection deviations caused by mixing samples from different stress areas and significantly improves the accuracy and repeatability of de-icing agent loss measurement results.

[0011] Preferably, in step S2, the solvent is deionized water, the soaking time is 24-48 hours, the temperature is controlled and the mixture is stirred regularly during the soaking process to ensure that the effective components of the de-icing agent are fully dissolved; after soaking, the extract is obtained by filtration with filter paper.

[0012] Preferably, in step S2, the characteristic ion is chloride ion, and the detection method is ion-selective electrode method. Each extract is detected in parallel at least 3 times, and the average value is taken as the final detection result.

[0013] Preferably, in step S2, at least three representative sampling points are selected for each partition, and road surface samples are obtained by core drilling in combination with the trajectory density distribution characteristics detected by the sensor.

[0014] Preferably, in step S3, when calculating the de-icing agent loss in each zone, the average value of the loss at all sampling points in each zone is taken as the final loss for that zone. At the same time, the correlation between the de-icing agent loss and the vehicle trajectory density is analyzed by combining the sensor trajectory density data at the corresponding locations of each sampling point.

[0015] Beneficial effects: This invention innovatively introduces the intensity of vehicle load influence into the evaluation system of de-icing agent loss on anti-icing roads, and uses trajectory density to characterize the intensity of load influence, realizing the mechanism and scientification of road lateral zoning, and overcoming the shortcomings of existing zoning methods that lack theoretical basis; This invention breaks through the limitations of overall average evaluation in the prior art. By sampling and testing in different areas, it achieves the quantitative determination of de-icing agent loss in wheel track areas and non-wheel track areas. This can accurately reflect the non-uniform distribution characteristics of lateral loss on anti-icing roads and improve the pertinence and reliability of the test results. The method for determining zonal loss constructed in this invention has clear, operable, and repeatable steps, making it suitable for actual testing in engineering sites. It can provide a solid quantitative basis for life prediction, precise maintenance, and differentiated maintenance of anti-icing roads, thereby reducing maintenance costs and extending road service life. The method of this invention is highly versatile and applicable to anti-icing roads of different types and service life. It does not require complex testing equipment, is easy to promote and apply, and has high engineering practical value. Detailed Implementation

[0016] The present invention will be described in detail below with reference to specific embodiments: Example 1

[0017] This embodiment takes an anti-icing asphalt road that has been in use for one year as the research object. The road has a lane width of 3.75m and a designed traffic volume of 3000 vehicles / day. It mainly carries small cars and light trucks. The de-icing agent used is a calcium chloride-based composite de-icing agent. The specific implementation steps are as follows: S1. Road Lateral Zoning: Using the centerline of the road lanes as the lateral reference, a lateral coordinate system is established. Video monitoring is used to record vehicle trajectories, and the density of vehicle tire trajectories on the lateral side of the road is statistically analyzed to determine the distribution pattern of vehicle load influence intensity. The trajectory density is acquired in real-time by sensors. The sensors continuously capture, record, and statistically analyze vehicle tire trajectories at different lateral positions on the road, accurately outputting the frequency of tire passage per unit length at each lateral point. This serves as the core data support for trajectory density, quantifying the intensity of vehicle load influence. Based on the trajectory density distribution characteristics, the road cross-section is divided into three regions: the main wheel track zone extends 0.45m to the left and right of the vehicle wheel track centerline; the extended wheel track zone extends from the outside of the main wheel track zone to positions 1.0m to the left and right of the wheel track centerline; the remaining areas are non-wheel track influence zones. S2. Zonal Sampling and Preprocessing: Three representative sampling points were selected in the main wheel track zone, the extended wheel track zone, and the non-wheel track influence zone. Based on the trajectory density distribution characteristics detected by the sensor, a road surface sample with a diameter of 100 mm and a depth of 50 mm (consistent unit volume) was obtained from each sampling point using the core drilling method, for a total of nine road surface samples. Each sample was crushed and ground to a particle size of less than 2 mm, and then dried in a 105℃ drying oven until the mass was constant. The dried mass of each sample was weighed using an electronic balance (accuracy 0.001 g) and recorded. S3. Extraction of de-icing agent components: Each dried sample was placed in 500 mL of deionized water and soaked at a constant temperature of 25°C for 48 hours. During this period, the sample was stirred once every 12 hours to ensure that the effective components of the de-icing agent were fully dissolved. After soaking, the sample was filtered with filter paper to obtain the de-icing agent extract of each sample. S4. Characteristic ion detection: The ion-selective electrode method is adopted, using a chloride ion-selective electrode and a reference electrode to determine the chloride ion concentration in each extract. Each extract is detected in parallel three times, and the average value is taken as the final detection result to ensure detection accuracy. S5. De-icing Agent Calculation and Evaluation: Based on chloride ion concentration, extract volume, sample mass, and the conversion relationship between calcium chloride and chloride ions, the de-icing agent loss per unit volume at each sampling point was calculated. The average value of three sampling points in each zone was then taken as the de-icing agent loss for that zone. Combined with the sensor trajectory density data at the corresponding locations of each sampling point, the de-icing agent loss data for the three zones were compared and analyzed. The results show that the de-icing agent loss in the main wheel track zone is 0.82 kg / m². 3 The extended wheel track zone is 0.45 kg / m. 3 The non-wheel track influence zone has a density of 0.18 kg / m². 3 The study clarified the distribution law of de-icing agent loss decreasing with the decrease of vehicle load intensity, and verified the effectiveness of the method. The test results of this embodiment show that the method of the present invention can accurately measure the amount of de-icing agent consumed in different transverse areas of the anti-icing road, clearly reflect the differences in consumption in each zone, and provide a reliable basis for the subsequent precise maintenance of the road. For example, maintenance measures such as supplementing the spraying of anti-icing materials in the main wheel track area can be taken to improve the overall anti-icing performance of the road.

[0018] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting loss of road release deicing material based on image perception, characterized in that: Includes the following steps: S1. Road Lateral Zoning: A lateral coordinate system is established with the road lane centerline as the lateral reference benchmark. The cumulative effect of vehicle tires on the road surface at different lateral positions is defined as the vehicle load influence intensity. This vehicle load influence intensity is used as the core basis for zoning, dividing the road cross section into the main wheel track zone, the extended wheel track zone, and the non-wheel track influence zone. The vehicle load influence intensity is characterized by the trajectory density of vehicle tires on the road lateral. The trajectory density is used to reflect the concentration of vehicle tire passage within a unit lateral length. The trajectory density data is obtained in real time through sensor detection. S2. Sampling, extraction and detection in zones: Obtain road surface samples with consistent unit area or unit volume in the three zones in step S1. After pretreatment of crushing, drying and weighing the samples, soak or rinse the samples in solvent to obtain snow melting agent extract. Then detect the content of characteristic ions representing the effective components of snow melting agent in the extract. S3. Calculation and evaluation of de-icing agent loss in each zone: Based on the characteristic ion detection results, combined with the sample mass, solvent volume, and conversion relationship between characteristic ions and effective components of de-icing agent, calculate the de-icing agent loss per unit area or unit volume in each zone, compare and analyze the differences in loss in each zone, and obtain the distribution characteristics of de-icing agent loss in the transverse direction of the road.

2. The image perception based road de-icing material loss detection method of claim 1, wherein: In step S1, the main wheel track zone extends 0.4-0.5m to the left and right, respectively, based on the center line of the vehicle wheel track; the extended wheel track zone is located outside the main wheel track zone and extends 0.8-1.1m to the left and right, based on the center line of the vehicle wheel track; the non-wheel track influence zone is the road area other than the main wheel track zone and the extended wheel track zone.

3. The image perception based roadway release deicing material loss detection method of claim 2, wherein: The boundary between the main wheel track area and the extended wheel track area is determined by the location where the vehicle tire track density detected by the sensor shows a significant decrease from the center to both sides.

4. The image perception based road release deicing material loss detection method of claim 1, wherein: In step S2, the solvent is deionized water, the soaking time is 24-48 hours, the temperature is controlled and the mixture is stirred regularly during the soaking process to ensure that the effective components of the de-icing agent are fully dissolved; after soaking, the extract is obtained by filtration with filter paper.

5. The method for detecting the loss of road slow-release anti-icing materials based on image perception according to claim 1, characterized in that: In step S2, the characteristic ion is chloride ion, and the detection method is ion-selective electrode method. Each extract is detected in parallel at least 3 times, and the average value is taken as the final detection result.

6. The image perception based road release deicing material loss detection method of claim 1, wherein: In step S2, at least three representative sampling points are selected for each partition, and road surface samples are obtained by core drilling in combination with the trajectory density distribution characteristics detected by the sensor.

7. The image perception based road release deicing material loss detection method of claim 1, wherein: In step S3, when calculating the de-icing agent loss in each zone, the average value of the loss at all sampling points in each zone is taken as the final loss for that zone. At the same time, the correlation between the de-icing agent loss and the vehicle trajectory density is analyzed by combining the sensor trajectory density data at the corresponding locations of each sampling point.