456results about How to "Short detection cycle" patented technology

The invention discloses a vehicle illegal parking detection system based on unmanned aerial vehicle inspection shooting, and relates to the technical field of vehicle illegal parking inspection. The vehicle illegal parking detection platform is in communication connection with an unmanned aerial vehicle image acquisition module, an image preprocessing module, a vehicle detection analysis module, an illegal parking judgment module, a license plate recognition module and an early warning notification module, and all the modules are in electric signal connection. According to the method, a large area can be rapidly covered through unmanned aerial vehicle inspection shooting, real-time monitoring and detection of vehicle illegal parking behaviors are achieved, compared with a traditional manual inspection mode, unmanned aerial vehicle inspection greatly shortens the detection period, the detection accuracy is remarkably improved, and the detection efficiency is improved through a high-precision image recognition algorithm. The vehicle illegal parking detection system can accurately recognize the license plate number, the vehicle type and the illegal parking position, and subjectivity and errors of manual judgment are effectively avoided.

The invention discloses a bridge welding quality detection method based on a gray scale-relative depth fitting visual technology, and relates to the technical field of bridge engineering quality detection.The bridge welding quality detection method comprises the following steps that S100, multi-dimensional image capturing is carried out, specifically, a high-resolution camera is used, and by means of illumination control and multi-angle shooting, a multi-dimensional image is obtained; according to the method, the image of the bridge welding part is comprehensively collected, rich original data is provided for subsequent detection, the high-resolution camera is used for capturing the multi-dimensional image, fine gray processing is combined, the image contrast and definition are effectively enhanced, key features of the welding part are highlighted, further, relative depth calculation is carried out by using the binocular vision principle, and the accuracy of the relative depth calculation is improved. The relative depth information of the welding part is accurately obtained, the problems that in a traditional method, the image quality is unstable, and the feature point extraction matching difficulty is large are solved, and the three-dimensional curved surface construction precision is remarkably improved.

The invention belongs to the technical field of anchoring strength detection, and discloses an underground anchor rod anchoring strength prediction method and system based on a support vector machine method. The method comprises the steps of cleaning data, processing missing values and standardizing features; using correlation analysis or other feature selection methods to select features with high correlation with anchoring strength; performing model training by using a support vector machine method SVM, and optimizing hyper-parameters by using grid search; completing the construction of a support vector machine model; and carrying out model evaluation and result visualization. Key parameters in the mixing process are monitored in real time through the sensor network, the system can obtain anchoring strength data in real time, the detection period is greatly shortened, and the construction efficiency is improved. The support vector machine algorithm is adopted to analyze and predict the collected data, personal errors can be eliminated to a greater extent, and the accuracy and reliability of anchoring strength detection are improved.

The invention discloses a multiple primer group for detecting pathogens of reproductive system diseases and a kit thereof. The kit comprises a combination of amplification primers and fluorescent probes of 10 pathogens, the pathogens comprise molluscum contagiosum virus, Snethia bacteria, gardnerella vaginalis, prevotella genus, campylobacter genus, giant globules genus, Atortobacter vaginalis, lactobacillus jensenii, lactobacillus crispatus and inert lactobacillus. The kit contains UDG enzyme and is used for preventing pollution of a PCR amplification product, and the accuracy of a detection result is ensured. The kit provided by the invention contains an internal reference control, can display the occurrence of false negative, and is used for monitoring the collection, transportation, extraction and amplification processes of a to-be-detected sample. According to the kit, the specificity is 100%, the detection sensitivity reaches 10 copies / mL, the specificity is good, and the detection efficiency is high. The kit is used for identifying the reproductive system disease pathogens of a to-be-detected sample, the to-be-detected sample can be vaginal secretion, the operation steps are simple, and the detection period is short.

The invention belongs to the technical field of microorganisms, and particularly relates to a microorganism detection culture device and method.The system comprises an intelligent environment control system, and a sensor monitors the temperature, humidity and gas concentration of a culture environment in real time to ensure that environment parameters needed by microorganism growth are kept in the optimal state; the automatic sample processing and operating system avoids pollution or errors caused by manual intervention through a mechanical arm or an automatic transmission device; the real-time image monitoring and analyzing system is used for monitoring the growth state of the microorganisms in the culture dish; the camera can continuously shoot images in the culture process; the rapid detection module rapidly obtains activity information of microorganisms, rapid detection is achieved, and time needed by traditional culture is shortened. According to the invention, accurate control, automatic operation and rapid detection are realized, and an efficient, safe and intelligent solution is provided for microbial culture and detection.

The invention provides an in-orbit integrated circuit aging fault diagnosis method and diagnosis system, and relates to the technical field of integrated circuit design, and the method comprises the steps: ground training: collecting characteristic parameters, carrying out the characteristic parameter processing based on an SPI protocol algorithm, a digital signal recovery fixed-point decimal algorithm, a wavelet transform filtering algorithm and a deviation standardization algorithm, and carrying out the fault diagnosis; constructing an aging failure fault data set, training through a back propagation algorithm to obtain feed-forward neural network parameters, and transplanting the feed-forward neural network parameters to a feed-forward neural network constructed based on Pytorch and a feed-forward neural network constructed based on FPGA for joint testing; and in-orbit testing: data enters a data buffer area, training data is provided for a learning training module, feedforward neural network parameters are periodically updated by using an exponential weighted moving average algorithm, and the feedforward neural network parameters are transplanted to a feedforward neural network established based on Pytorch and a feedforward neural network established by an FPGA for joint testing. Continuous learning of on-orbit data is realized, and the method adapts to irregular periodic electromagnetic influence of a space environment.

The invention belongs to the technical field of whole-genome gene chips, and particularly relates to a leymus chinensis whole-genome 50K liquid chip and application thereof. According to the leymus chinensis whole genome 50K liquid phase chip, a whole genome re-sequencing technology is utilized, an SNP molecular marker combination of leymus chinensis is determined, and then the leymus chinensis whole genome 50K liquid phase chip is developed. The chip is combined with a high-density SNP probe, realizes high-throughput and high-precision genetic typing of leymus chinensis genomes, and particularly has wide application prospects in the field of molecular breeding.

The invention discloses a dry-type transformer testing device, and relates to the technical field of testing devices, the dry-type transformer testing device comprises a workbench and a transformer main body, the transformer main body is installed on the workbench, the transformer main body is provided with a wiring terminal, and one side of the workbench is provided with a testing assembly for testing the transformer main body. According to the dry-type transformer testing device, when the brush is in contact with the outer wall of the wiring terminal and rotates, dirt, dust or other impurities on the outer wall of the wiring terminal can be effectively cleared away, through the clearing work, the insulation resistance tester body can check and measure the wiring terminal more accurately, and the testing efficiency of the dry-type transformer is improved. Therefore, potential connection problems, corrosion or abrasion and the like can be found easily, misjudgment or missing detection is avoided, and after interference factors of dirt and impurities are eliminated, the accuracy and reliability of a detection result can be improved.

The invention discloses a complex pavement damage condition detection method integrated with an unmanned aerial vehicle. The method specifically comprises the following steps: S1, an unmanned aerial vehicle platform and a sensor system; s2, image preprocessing and feature extraction; the invention relates to the technical field of intelligent traffic. According to the complex road surface damage condition detection method and device fused with the unmanned aerial vehicle, the unmanned aerial vehicle is high in inspection speed and wide in coverage range, the detection period is greatly shortened, the detection cost is reduced, and artificial intelligence technologies such as deep learning and machine learning are combined, so that accurate recognition and classification of road surface damage conditions are realized, and the detection precision is improved; a comprehensive detection report and three-dimensional visual analysis are provided, a scientific basis is provided for road maintenance decision, traffic safety management and urban planning, road maintenance is promoted to develop towards the intelligent and precise direction, the road maintenance efficiency and quality are improved, traffic safety is guaranteed, an interactive decision support system is developed, and the development of the road maintenance system is facilitated. And the user operation convenience and the decision-making efficiency are improved.

The invention relates to the technical field of railway line flaw detection, in particular to railway line automatic comprehensive flaw detection equipment which comprises two steel rails and a flaw detection car and further comprises a C-shaped mounting plate, a buffer assembly, a waist detection mechanism and a bottom detection mechanism. A first fixing rod is transversely arranged in the C-shaped mounting plate, fixing sleeves are slidably connected to the two ends of the first fixing rod correspondingly, and balance assemblies are arranged at the ends, away from each other, of the two fixing sleeves correspondingly. The device has the advantages that different detection assemblies are arranged for different detection areas, so that steel rail detection of different parts is more efficient and accurate, it is ensured that different types of defects can be detected, the detection period is shortened, the balance assembly and the buffer assembly cooperate with each other to enable the head detection assembly to keep a dynamic balance state, and the detection efficiency is improved. The probe is effectively protected from vibration damage, and the adaptability of the probe to the change of the steel rail and the detection stability of the probe are improved.

The invention discloses a variable splicing marker for tuberculosis diagnosis, a screening method and application, and relates to the technical field of marker screening, and the technical key points are as follows: the variable splicing marker is AIM2-Intron3-IR; by combining bioinformatics analysis, in-vitro experiments and clinical queue verification analysis of a large amount of high-throughput sequencing data, variable splicing markers closely related to tuberculosis are mined and determined, and a high-efficiency new tool is provided for tuberculosis diagnosis.

The invention relates to the technical field of flexible lamp strips, and discloses an FPC-COB flexible lamp strip, a bending degree detection device and a production method.The FPC-COB flexible lamp strip bending degree detection device comprises an installation shell and further comprises a winding wheel connected to the interior in a sliding mode, a transmission belt drives a concave-convex rotating wheel to rotate randomly when rotating, and the transmission belt drives the concave-convex rotating wheel to rotate randomly when rotating; when the pusher stretches, the transmission strip set is pushed through the concave-convex rotating wheel, and therefore the heights of the first upper pressing roller, the first lower pressing roller, the second upper pressing roller and the second lower pressing roller are changed to conduct bending detection on the soft lamp strip. Through rotation of the transmission belt, the concave-convex rotating wheels are in a random rotation state due to inertia after being driven, so that the distances between the concave-convex rotating wheels and the transmission strip group change randomly, it is guaranteed that different positions of each soft lamp strip can be detected at random angles, the weakness of the lamp strip at a specific bending angle can be rapidly recognized, and the detection accuracy is improved. Therefore, adjustment and optimization are carried out, and the effective and random detection effect during automatic production is met.

The invention relates to a woolen sweater defect detection method. The method is characterized by comprising the following four main steps: a data preprocessing step, a model construction step, a model training step and a defect detection step. Wherein in the model construction step, strategies such as an attention mechanism and a network structure optimization are introduced, so that the identification capability of the model on woolen sweater defects is improved; in the detection process, automatic positioning and classification of defect areas are realized through a series of key functions. The method is high in detection efficiency, low in comprehensive detection cost and high in detection accuracy.

The invention relates to the technical field of compressive strength prediction of desert sand hybrid fiber cement-based composite materials, in particular to a 28d strength prediction method of a desert sand hybrid fiber cement-based composite material, which comprises the following steps: performing formula fitting on compressive strength at different ages; the function expression between the compressive strength and the number of maintenance days is # imgabs0 #, T is the number of maintenance days, and sigma c is the compressive strength of the corresponding age; a < lambda > and b < lambda > are regression coefficients considering the influence of fiber types and relative mixing amounts, the influence of fiber influence factors on a < lambda > and b < lambda > is considered, a fitting formula of sigma < c > about the fiber influence factor < lambda > and the curing age T is obtained, and lambda and T = 28 are substituted into a prediction formula # imgabs1 #, so that the 28d compressive strength of the desert sand hybrid fiber cement-based composite material can be determined. The prediction method is feasible and effective, and can accurately and quickly predict the 28d compressive strength of the desert sand hybrid fiber cement-based composite material, so that the detection period is shortened.

The invention relates to the technical field of environmental protection, in particular to a solid waste detection method and system.The method comprises the steps that solid waste samples with disclosed categories, components and contents and Raman spectrum data corresponding to the solid waste samples are obtained from the Internet to serve as a training set, and the training set is used for training an R-CNN-based target detection model; after training is completed, the flooding ability and performance of the model are evaluated through cross validation; after verification is completed, solid waste samples are prepared and subjected to sample pretreatment; after the sample pretreatment is completed, scanning the sample by using a Raman spectrometer to obtain Raman spectrum data of the sample, and then carrying out data pretreatment; after data preprocessing is completed, Raman spectrum data are input into the verified R-CNN-based target detection model, and a detection result is output. According to the method, the information of the solid waste is accurately detected through a Raman spectrum and machine learning combined method, technical support is provided for management of the solid waste, environmental pollution is reduced, and the ecological environment is protected.

The invention relates to the field of detection methods, and particularly discloses a method for determining the silicon content in silicon steel through inductively coupled plasma emission spectrometry. The determination method comprises the following steps: preparing a to-be-tested solution: weighing 0.10-0.20 g of a silicon steel sample, adding 10-15mL of nitric acid (1 + 2), heating and dissolving at a low temperature, boiling for 10-60s, cooling to room temperature, keeping the temperature of the solution to be less than or equal to 80 DEG C, dropwise adding 3-5mL of hydrofluoric acid, and fixing the volume to obtain the to-be-tested solution; 0.095-0.19 g of metal iron is weighed and put into a polytetrafluoroethylene beaker, and the metal iron is dissolved according to the sample dissolving method in the prepared to-be-tested solution to obtain the Fe reference solution; making a standard curve: mixing the Si reference solution and the Fe reference solution with different volumes to prepare a standard solution in an analysis gradient range, and detecting and drawing the standard curve; and detecting the to-be-detected sample, and combining the standard curve to obtain the silicon content in the silicon steel sample. The method has the advantages of high accuracy, simplicity, convenience, rapidness and short detection period.

The invention discloses a precision detection device for valve gearbox gear production, and the device comprises a detection platform which comprises a detection structure and a lifting structure which are disposed on a platform surface, and a feeding assembly which is disposed at the front side of the detection platform and is used for intermittently conveying gears to the rear side, the feeding assembly comprises an electric conveying frame and a belt installed on the electric conveying frame, a plurality of spacing bars arranged at equal intervals are installed on the belt, and guide plates used for guiding a to-be-detected gear to be discharged backwards along the central axis are connected to the two sides of the electric conveying frame in an inserted mode. And the distance between the bottom of the guide plate and the top face of the belt is larger than the height of the spacing bar, and the conveying structure is arranged on the rear side of the feeding assembly. In the whole operation process, the manual intervention time can be effectively shortened, the detection period is shortened, meanwhile, safety is improved, in addition, bidirectional clamping and fixing can be conducted along the inner ring and the outer side of the gear to be detected, and the stability of follow-up rotation of the gear to be detected and the reliability of a detection result are guaranteed.

The invention discloses a system and a method for intelligently detecting foreign matters in a rail transit interval. The system comprises a data acquisition unit, a data transmission unit, an image processing and analyzing unit, a foreign matter algorithm unit, an alarm processing unit, a data recording unit and a user interface unit, the data acquisition unit is installed on a track line and is used for carrying out continuous image acquisition on a traffic interval and detecting abnormal conditions on a track in real time. The data transmission unit is used for transmitting the collected information; the image processing and analyzing unit is used for processing and analyzing the acquired data and identifying possible foreign matters; the foreign matter algorithm unit is used for identifying and positioning possible foreign matters; when the alarm processing unit detects the foreign matter, an alarm is given to related personnel in time; the data recording unit is used for recording all foreign matter detection data, the user interface unit is used for feeding back the obtained foreign matter information, monitoring and detection operation can be automatically carried out, manual intervention is not needed, and therefore efficiency is improved, and labor cost is reduced.

The invention relates to an SPC monitoring device for engine valve production, and relates to the field of automobile engine part manufacturing, the SPC monitoring device comprises a machine table, a supporting seat is installed on the table top of the machine table, a through groove of the supporting seat is used for placing an engine valve rod, a rotating shaft is rotatably arranged on one side of the supporting seat, and a first vertical plate is arranged on the other side of the supporting seat; a sliding table is arranged on the first vertical plate, a sliding plate is arranged above the sliding table, and a ball seat is mounted on the sliding plate; a transverse plate is arranged at the top of the second vertical plate, a bearing plate sliding transversely is arranged on the outer side plate face of the transverse plate, and a first air cylinder and a second air cylinder are arranged on the two sides of the bottom of the bearing plate respectively and provided with a first curvature probe and a second curvature probe respectively. On one hand, manpower can be saved, the labor amount of operators can be effectively reduced in the whole process, needed manpower is reduced, on the other hand, the operation efficiency can be improved, the monitoring time of a single engine valve is further shortened, and the system can effectively adapt to a high-frequency and uninterrupted engine valve production line.

The invention provides a virus RNAi inhibitor screening vector as well as a construction method and application thereof, and belongs to the technical field of viruses. The virus RNAi suppressor screening vector comprises a green fluorescent protein coding gene expression cassette and a shRNA expression cassette targeting a green fluorescent protein coding gene, the nucleotide sequence of the shRNA is as shown in SEQ ID NO: 2 or SEQ ID NO: 3. According to the invention, the green fluorescent protein coding gene and the shRNA are cloned into the same expression vector, so that the consistency of transfection efficiency is ensured. The invention further provides a screening method of the virus RNAi suppressor, the virus RNAi suppressor screening vector and the recombinant vector for expressing the virus RNAi suppressor to be screened are co-transfected into cells, and the screening result is judged according to the green fluorescence intensity. According to the technical scheme, operation is easy and convenient, the screening result is accurate and reliable, and wide application and popularization value is achieved.

The invention relates to a motor shell rusty spot recognition method, device and equipment and a computer readable medium. The method comprises the following steps: acquiring a first motor shell image before a salt spray test and a second motor shell image after the salt spray test; performing image registration on the first motor shell image and the second motor shell image to perform registration on a rusty spot area in the first motor shell image and a rusty spot area in the second motor shell image to obtain an effective motor shell image; performing difference detection on the effective motor shell images before and after the salt spray test, and extracting a newly added rusty spot area based on a difference detection result; constructing a rusty spot recognition model according to the newly added rusty spot area and the corresponding motor shell image; based on the rusty spot recognition model, the rusty spot type of the newly-added rusty spot area is recognized, and the area corresponding to the newly-added rusty spot area is calculated. According to the invention, the problem of low detection efficiency and accuracy caused by manual motor shell rusty spot recognition detection is solved.

The invention discloses a variable splicing marker for predicting the progress of tuberculosis and application thereof, and relates to the technical field of markers for predicting the progress of tuberculosis and application thereof, and the technical key points are that the variable splicing markers are DNASE1L2-IR and PHF11-IR, and the markers are used for predicting the progress of tuberculosis; the variable splicing markers closely related to latent tuberculosis and progressive tuberculosis are mined and determined by combining bioinformatics analysis and in vitro experiments of a large amount of high-throughput sequencing data, and a high-efficiency new tool is provided for predicting the progress of tuberculosis.

The embodiment of the invention provides a display device and electronic equipment. According to the display device, a display panel comprises a first binding part and a second binding part, one end of a driving chip is in binding connection with the first binding part, a flexible circuit board is connected with the other end of the driving chip through the second binding part, and the first binding part comprises at least one first invalid pin and at least one test line; the first invalid pin is connected with the test line, so that detection can be performed through the test line when a signal transmitted by the display device is detected, the test line is a routing line in the display panel, the accuracy of the detected signal is relatively high, the display device can be directly detected, a flexible circuit board does not need to be tested, the detection period is relatively short, and the detection efficiency is relatively high. And the detection efficiency and accuracy are improved.

The invention discloses a river channel water dissolved oxygen concentration change prediction model and a construction method and application thereof, and is characterized in that a dissolved oxygen prediction method comprises the following steps: monitoring a predicted target water body to obtain a dissolved oxygen data sample of the target water body; performing PARAFAC analysis on the EEM data of the dissolved organic matters in the target water body to obtain result data of each fluorescent component; analyzing the result data of each component after the PARAFAC analysis so as to determine the tryptophan component of the high oxygen consumption substance; constructing a novel dissolved oxygen kinetic model based on the tryptophan-like component; the dissolved oxygen prediction model is used for predicting the dissolved oxygen of the water body, and has the advantages that the test and data processing method of the model is simple, rapid and efficient, the classic model is more practical and accurate, the model can be used for predicting the attenuation trend of the dissolved oxygen in the polluted river channel, and reliable technical support is provided for monitoring abnormal fluctuation of the dissolved oxygen in the river.

The invention relates to the technical field of urinary tract infection detection, and discloses a phage-based rapid and accurate urinary tract infection detection method, which comprises the following steps: S1, constructing a CRISPR / Cas9 system; s2, constructing a homologous recombinant plasmid; s3, constructing three plasmids BL21; s4, constructing, screening, verifying and reporting a phage T4:: Nluc; according to the detection method, a CRISPR / Cas9 system is combined with a homologous recombination technology, so that the efficiency of bacteriophage gene editing is remarkably improved, based on the report bacteriophage T4:: Nluc, rapid, sensitive, specific, accurate, simple and convenient detection on clinical urinary tract infection escherichia coli is realized, and the method can be used for detecting an escherichia coli urinary tract infection sample which is not pretreated, and has a wide application prospect. The kit shows excellent detection timeliness and specificity, is a powerful tool for identifying clinical urinary tract infection escherichia coli, and provides a new technical path for timely and accurate diagnosis and effective treatment of clinical urinary tract infection.

The invention discloses a camellia oleifera SNP (Single Nucleotide Polymorphism) molecular marker combination and application thereof. The camellia oleifera SNP molecular marker combination comprises at least one of 50 SNP molecular markers and 454 SNP molecular markers, and the physical positions of the 50 SNP molecular markers and the 454 SNP molecular markers are determined on the basis of sequence alignment of a camellia oleifera reference genome GCA039905865.1. The camellia oleifera SNP molecular marker combination can be effectively used for molecular marker breeding of camellia oleifera, the camellia oleifera SNP molecular marker combination is used for preparing a first type of whole genome liquid phase chip of camellia oleifera, and the application cost of genetic diversity analysis, QTL positioning, GWAS analysis and the like in scientific research of camellia oleifera is effectively reduced; the problem that no applicable product exists in camellia oleifera molecular auxiliary breeding and whole genome selective breeding is solved, and the process of camellia oleifera basic research and breeding is accelerated.

The invention discloses an SNP (Single Nucleotide Polymorphism) molecular marker combination related to a peanut high-oil character and application of the SNP molecular marker combination, and belongs to the technical field of biological breeding, the SNP molecular marker combination provided by the invention consists of 538 SNP sites, and physical position information of the SNP site combination is determined based on comparison of a whole genome sequence of a peanut reference genome; and the version number of the whole genome sequence of the peanut reference genome is GCA004170445.1. The SNP molecular marker combination provided by the invention can be applied to molecular breeding work of peanuts, germplasm resources can be rapidly analyzed, and whether a breeding parent material contains high-oil allelic variation or not can be determined.

The utility model discloses intelligent detection equipment for rice quality. A first stock bin, a first metering scale, a cleaning sieve, a first conveyor, a second stock bin, a second metering scale, a second conveyor, a third stock bin, a rice hulling and grinding combined machine, a white rice sieve, a third conveyor, a fourth stock bin, a third metering scale, a fourth conveyor, a fifth stock bin, a division instrument and a rice analyzer are sequentially connected in series; a series of indexes such as the impurity content, the rice yield, the finished product yield and the appearance quality index of a rice sample can be detected at a time, so that the omnibearing detection requirement of the rice quality is met, integration of rice quality detection equipment is realized, the detection efficiency is improved, and the detection period is shortened. Moreover, the second metering scale and the third metering scale are used for quantitatively weighing the materials and then discharging the materials in batches, so that the weighing and metering requirements are met, the subsequent processing pressure and the division detection pressure are respectively relieved, the continuous and rapid circulation of the whole detection process is ensured, and the detection efficiency is improved.

The invention discloses an active tuberculosis diagnosis method based on proteomics and metabonomics. The method comprises the following steps: collecting a serum sample of a suspected active tuberculosis patient; detecting specific proteins in the serum sample by using a proteomics technology; detecting specific amino acids in the serum sample by using a metabonomics technology; carrying out standardization treatment on the obtained protein, amino acid and bile acid data; establishing a diagnosis model by using a machine learning algorithm; inputting data of a to-be-diagnosed sample into the model, and calculating a probability value of the to-be-diagnosed sample suffering from active tuberculosis; according to a preset threshold value, whether the sample provider suffers from the active tuberculosis or not is judged. By comprehensively analyzing multiple omics data, the sensitivity and specificity of diagnosis are greatly improved; by utilizing a machine learning algorithm, the disease risk can be objectively and quantitatively evaluated, and human misjudgment is reduced; the detection process is simple and fast, the sample demand is small, and clinical popularization and application are facilitated.

The invention discloses a gear damage detection method and device, electronic equipment and a storage medium, and the main technical scheme comprises the steps: obtaining a to-be-recognized gear detection image, inputting the to-be-recognized gear detection image into a U-Net neural network, and obtaining a binary image mask of the to-be-recognized gear detection image outputted by the U-Net neural network; performing phenotype quantization on the binarized image mask to obtain a phenotype actual area of the foreground object; and determining that the phenotype actual area is a gear damage area, and determining whether the gear is damaged or not according to a preset gear damage area threshold value. According to the method, a to-be-recognized gear detection image is input into a U-Net neural network containing a residual structure, and an obtained binary image mask clearly distinguishes a foreground from a background, so that phenotype quantization is more accurate, and the gear damage area is accurately calculated. The gear damage detection accuracy is improved, the detection period is greatly shortened, the maintenance cost is reduced, and the production efficiency is improved.