The invention belongs to the field of depth learning, and provides a rail surface defect detection method based on the depth learning network, aiming at solving various problems existing in the priorrail detection methods. The depth learning method first automatically resets the input rail image to 416*416, and then extracts and processes the image. Image extraction mainly by Darknet-53 model complete. The processing output is mainly accomplished by the FPN-like network model. Firstly, the rail image is divided into cells. According to the position of the defects in the cells, the width, height and coordinates of the center point of the defects are calculated by dimension clustering method, and the coordinates are normalized. At the same time, we use logistic regression to predict the fraction of boundary box object, use binary cross-entropy loss to predict the category contained in the boundary box, calculate the confidence level, and then process the convolution in the output, up-sampling, network feature fusion to get the prediction results. The invention can accurately identify defects and effectively improve the detection and identification rate of rail surface defects.

Rail surface defect detection method based on depth learning network

The invention discloses a human image segmentation method. The method is suitable for being executed in a mobile terminal, and comprises the following steps of: segmenting a to-be-processed image by utilizing a predetermined segmentation network, wherein the predetermined segmentation network comprises an encoding stage and a decoding stage, the encoding stage comprises a first number of pairs ofconvolution layers and down-sampling layers, which are connected in sequence, and each pair of convolution layer and down-sampling layer forms a convolution-down-sampling pair; outputting a down-sampling characteristic pattern of the encoding stage after carrying out iteration through a first number of convolution-down-sampling pairs, wherein the decoding stage comprise a first number of pairs ofconvolution layer and up-sampling layers, which are connected in sequence, and each pair of convolution layer and up-sampling layer forms a convolution-up-sampling pair; and outputting a segmentationresult map after carrying out iteration through the first number of convolution-up-sapling pairs. The invention furthermore discloses the corresponding mobile terminal.

Human image segmentation method and mobile terminal

The invention discloses a method and system for monitoring a water gauge water level video based on a deep learning algorithm, and belongs to the technical field of image identification and water level monitoring. The method comprises the steps of video acquisition, video frame processing, water level line identification, water level measurement and the like. The method and system for monitoring the water gauge water level video based on the deep learning algorithm have the advantages that a deep learning neural network is used for achieving the intellectualization and automation of the waterlevel monitoring, very high accuracy and feasibility are provided, the structure is simple, the device is arranged conveniently, compared with the prior art, the cost is low, the automation level is high, higher monitoring efficiency is provided, and the method is an important improvement for the prior art.

Method and system for monitoring water gauge water level video based on deep learning algorithm

The invention belongs to the field of computer vision and deep learning. The method specifically relates to a bridge pavement crack detection and segmentation method based on semantic segmentation, and the method comprises the steps: firstly performing artificial segmentation of the samples in a data set, and making labels of training samples; secondly expanding the number of images in the data set by data enhancement; thirdly inputting a prepared training set into an FC-DenseNet 103 network model for training; and finally extracting cracks through a collected crack image of the test set. Mostof conventional crack detection methods employ edge detection, morphology and thresholding methods and need the manual setting and adjustment of parameters. Currently known deep learning methods areall built on the basis of small influence from noise and clear crack targets, and underestimate the complexity of a bridge pavement image, and are difficult to meet the demands of engineering applications. The method is combined with a sematic segmentation algorithm, and is suitable for the complex background.

Bridge pavement crack detection and segmentation method based on semantic segmentation

The invention provides a road surface defect detection method based on texture feature extraction. The road surface defect detection method comprises the following steps that an image having a road surface defect is acquired and grayscale processing is performed so as to form a road surface defect grayscale image; texture feature extraction is performed on the road surface defect grayscale image,the feature values are extracted to form texture feature vectors and the road surface defect grayscale images represented by the texture feature vectors of the same defect class are equally divided soas to form a training set and a test set; high-dimensional abstract feature extraction is performed on the training set through a stack type auto-encoder; and a softmax layerer logical classificationlayer is stacked in the stack type auto-encoder to form a deep neural network, the high-dimensional abstract features are trained through the deep neural network and classification and identificationof the road surface grayscale images of the test set are completed. The road surface defect can be accurately detected and the accuracy of the result can be enhanced.

Road surface defect detection method based on texture feature extraction

The invention discloses an image crack segmentation method based on a deep convolutional neural network. The image crack segmentation method comprises the steps that an original image is inputted to the deep convolutional neural network, and a characteristic graph is acquired through convolution, pooling and activation layer characteristic learning; sampling is performed on the characteristic graph so as to obtain the characteristic graph having the same size with that of the original image; and softmax prediction is performed on the characteristic graph having the same size with that of the original image so that the category of the corresponding position can be acquired, and crack area segmentation can be realized. Multilevel characteristic from the low level to the high level can be learned, high-precision crack area segmentation can be rapidly realized and the method is particularly suitable for bridge structure crack detection.

Image crack segmentation method based on deep convolutional neural network

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Image crack segmentation method based on deep convolutional neural network