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

The invention discloses a pedestrian and vehicle detection method and system based on improved YOLOv3. According to the method, an improved YOLOv3 network based on Darknet-33 is adopted as a main network to extract features; the cross-layer fusion and reuse of multi-scale features in the backbone network are carried out by adopting a transmittable feature map scale reduction method; and then a feature pyramid network is constructed by adopting a scale amplification method. In the training stage, a K-means clustering method is used for clustering the training set, and the cross-to-parallel ratio of a prediction frame to a real frame is used as a similarity standard to select a priori frame; and then the BBox regression and the multi-label classification are performed according to the loss function. And in the detection stage, for all the detection frames, a non-maximum suppression method is adopted to remove redundant detection frames according to confidence scores and IOU values, and an optimal target object is predicted. According to the method, a feature extraction network Darknet-33 of feature map scale reduction fusion is adopted, a feature pyramid is constructed through feature map scale amplification migration fusion, and a priori frame is selected through clustering, so that the speed and precision of the pedestrian and vehicle detection can be improved.

A pedestrian and vehicle detection method and system based on improved YOLOv3

The invention belongs to the field of computer vision, particularly relates to a multi-label object detection method, system and device based on a target detection network, and aims at solving the problems that an existing target detection technology is low in precision, and real-time accurate detection of a multi-label object cannot be well achieved. The method comprises the steps of obtaining animage of a to-be-detected object as an input image; the trained target detection network is adopted, the category of the to-be-detected object and the center coordinate of the to-be-detected object are obtained according to the input image, and the length and width of the rectangular frame are detected; and the category of the to-be-detected object and the central coordinate of the to-be-detectedobject are output, and the length and width of the rectangular frame are detected. According to the method, real-time and accurate detection of different types of targets is realized, the detection speed and precision are improved, and the conditions of mutual overlapping and shielding in target detection can be well processed.

Multi-label object detection method, system and device based on target detection network

The invention discloses a metal shaft surface defect identification method based on deep learning, and the method comprises the steps: scanning the surface of a metal shaft through a linear array camera, and obtaining the global image data; Carrying out defect image pre-screening; Preprocessing the image, including denoising and sharpening; Enhancing the image data to obtain a large amount of richimage data; Making a data set again, and carrying out labeling through labeling software to generate a label file; Dividing a data set, and dividing the data set into a training set, a verification set and a test set; Converting the data set into a TFRecore format; Inputting the data in the TFRecore format into a deep learning target detection model for training; Model evaluation; Deriving a computational graph; And testing the performance of the model on the test set. According to the invention, a deep learning technology and metal shaft surface defect detection are combined, the method hasobvious advantages in precision and generalization ability, and high-efficiency detection of metal shaft parts is realized.

metal shaft surface defect identification method based on deep learning

The invention discloses a weld seam and weld seam defect detection method based on deep learning. A YOLOV3 network is used to realize weld seam and/or weld seam defect detection. A network training step comprises the following steps of using a positioning frame to select and mark the weld seams in a workpiece image and taking as a training data set; using the positioning frame to select weld defects and mark defect types in a weld seam image, and taking as a training data set I; acquiring coordinates xp and yp of the positioning frame, and width and height sizes wp and hp; initializing a network; randomly acquiring an input tensor aj for training calculation, and outputting a detection result; calculating an error function loss of a prediction result by using the detection result; and adjusting a weight W and an offset value b in combination with a gradient descent method, repeating in the way, and acquiring a trained network. The multiple weld seams and the multiple defect types can be synchronously detected, weld seam identification positioning and defect detection can be realized in one measurement, and measurement efficiency and accuracy are effectively increased.

Weld seam and weld seam defect detection method based on deep learning

The invention relates to a defective insulator identification method based on a YOLOv3 network and a particle filter algorithm According to the defective insulator identification method based on a YOLOv3 network and a particle filter algorithm, the defect of an insulator in an inspection image containing a complex background in a real environment is identified based on a YOLOv3 network and a particle filter algorithm The efficiency of insulator defect identification is greatly improved, and a reliable technical support is provided for the power grid maintenance department

Defective insulator identification method based on YOLOv3 network and particle filter algorithm

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Defective insulator identification method based on YOLOv3 network and particle filter algorithm