The invention discloses a method for identifying a loss fault of an assembled nut of a cross beam of a railway wagon, solves the problems of missing detection and wrong detection caused by detecting the loss fault of the assembled nut of the cross beam in an existing manual image inspection mode, and belongs to the field of fault detection of railway wagons. The method comprises the following steps: constructing a triangular hole sample set and a nut sample set; training the two sample sets by using a semantic segmentation-based U-shaped deep learning network to obtain semantic segmentation-based U-shaped deep learning network optimal weights corresponding to the two sample sets; intercepting a triangular hole and a triangular hole nut part of a to-be-detected spanning beam image, and obtaining the to-be-detected sub-image; inputting the to-be-detected sub-image into the U-shaped deep learning network to obtain a triangular hole binary image; and when a screw support is arranged at thetriangular hole and no cotter pin exists in the nut binary image, further judging whether a nut exists in the nut binary image or not, if so, giving a cross beam assembly nut loss fault alarm, and ifnot, waiting for the next to-be-detected cross beam image.

Method for identifying loss fault of assembled nut of cross beam of railway wagon

The invention discloses a railway wagon small part bearing stop key nut looseness and loss fault detection method, and belongs to the technical field of freight train detection The objective of the invention is to solve the problems of high cost, low efficiency and the like of a current detection mode depending on manual image viewing and the problem of low accuracy of detection by an existing image automatic processing technology The method comprises the following steps: acquiring an image, extracting a bearing blocking key region image, constructing a sample data set, and training a deep learning network by using the sample data set to obtain a trained deep learning network; extracting a bearing blocking key area image for a real vehicle passing image, and obtaining a three-value segmentation image by using the trained deep learning network; carrying out fault detection by utilizing the three-value segmentation image, and if no nut exists in a bolt area, indicating that the stop key nut is lost; if partial area of the bolt is above the nut, indicating that the stop key nut is loosened; and if the situation is detected, carrying out fault alarming The method is mainly used forbearing stop key nut looseness and loss fault detection

Railway wagon small part bearing stop key nut looseness and loss fault detection method

The invention provides an unmanned aerial vehicle ground moving target recognition and image enhancement system and method. The unmanned aerial vehicle ground moving target recognition and image enhancement system is composed of an unmanned aerial vehicle, a ground image receiving and collecting module and a ground PC, wherein the unmanned aerial vehicle collects a ground real-time video through the camera and transmits the video to the ground image receiving and collecting module; the ground image receiving and collecting module transmits received video data to the ground PC through the USB port; the ground PC detects and recognizes a target in the video; and after the target is recognized, image resolution enhancement processing is conducted on a video image. According to the unmanned aerial vehicle ground moving target recognition and image enhancement system and method, real-time detection and identification of a ground moving target on an unmanned aerial vehicle platform can be indirectly completed; the phenomenon that the definition of the recognized image is poor due to complex illumination changes; camera shaking and other reasons is overcome; and great convenience is provided for subsequent analysis and decision making.

Unmanned aerial vehicle ground moving target recognition and image enhancement system and method

The invention discloses a loss and fault target detection method for swing bolster springs of a railway wagon, belongs to the field of image processing, and aims to solve the problems of false detection, missing detection and low efficiency in manual detection of loss and faults of swing bolster springs The method comprises the following steps of 1, collecting passing images of the swing bolstersprings of the railway wagon as to-be-detected images; 2, inputting the to-be-detected images in the step 1 into a target classification detection model for classification judgment; 3, judging whether the confidence coefficients of the lost swing bolster springs of the to-be-detected images are greater than a lost set threshold value or not, and if so, carrying out fault alarm; if not, executingthe step 4; 4, judging whether the confidence coefficients of the to-be-detected images which are a normal swing bolster spring are greater than a normal set threshold value or not, if so, returningto execute the step 1, and continuing to process next detection images; and if not, carrying out fault alarm

loss and fault target detection method for swing bolster springs of railway wagon

The invention discloses a chromosome division phase positioning and sequencing method, and belongs to the technical field of chromosome recognition. The chromosome division phase positioning and sequencing method comprises the following steps: 1) establishing an identification model based on a darknet53 basic network; 2) obtaining an independent chromosome image, and inputting the image accordingto a specified size; 3) performing feature extraction on the input chromosome image by using an identification model, setting three downsampling scales for the structure of the identification model, setting three priori boxes for each downsampling scale, and clustering the priori boxes with nine sizes in total; and 4) carrying out frame prediction by using the output of logistic. According to theinvention, an identification model is established based on a darknet53 basic network; characteristic extraction is performed on the input chromosome image by using the recognition model, and frame prediction is performed quickly, so that the chromosome type can be recognized accurately and efficiently, and compared with the existing recognition technology, the analysis efficiency of the chromosomekaryotype can be effectively improved, the recognition sorting time is shortened, and the chromosome sorting is completed with high accuracy.

Chromosome division phase positioning and sequencing method

The invention provides a D-series high-speed train carriage bottom plate bolt fault detection method based on deep learning, and relates to the field of image processing and deep learning, and the method comprises the following steps: manufacturing a to-be-detected D-series high-speed train carriage bottom plate bolt image database which comprises a D-series high-speed train carriage bottom platebolt graph-containing database and a bolt loss graph database; carrying out target positioning on the small bolts at the bottom of the carriage of the D-series high-speed train based on an improved yogov3 target detection algorithm; detecting the loss state of the to-be-detected bolt by adopting a deep learning-based method; and finally inputting the to-be-tested image into the detection model toobtain a bolt loss detection result. The detection efficiency of warehousing maintenance of the D-series high-speed train can be effectively improved, and the manpower input cost is reduced.

D-series high-speed train carriage bottom plate bolt fault detection method based on deep learning

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D-series high-speed train carriage bottom plate bolt fault detection method based on deep learning