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

The invention discloses a system for quickly recognizing functional regions in an apartment rendering, comprising image processing system module 100, image recognition and detection system module 200 and apartment rendering cloud service system module 300, wherein the image recognition and detection system module is connected with the image processing system module, and the apartment rendering cloud service system module is connected with the image recognition and detection system module. The system disclosed herein can recognize functional region image information in the apartment rendering reliably and quickly, the needs of people for searching for functional formation of the apartment rendering are met, people's work and living quality are improved, and the system is easy to widely apply and is of important production and practical significance.

System for quickly recognizing functional regions in apartment rendering

Embodiments of the invention provide a vehicle appearance feature recognition method and device, a vehicle retrieval method and device, a storage medium and electronic equipment. The vehicle appearance feature recognition method comprises the following steps of: obtaining a plurality of area segmentation results of a target vehicle in a to-be-recognized image; extracting global feature data and aplurality of pieces of area feature data from the to-be-recognized image on the basis of the plurality of area segmentation results; and fusing the global feature data and the plurality of area feature data so as to obtain appearance feature data of the target vehicle. Through the vehicle appearance feature recognition method, appearances of vehicles can be described more correctly. Moreover, recognized vehicle appearance features of different vehicle images can be directly compared, so that the problem that different areas between different vehicle images cannot be compared is solved.

Vehicle appearance feature recognition method and device, vehicle retrieval method and device, storage medium and electronic equipment

The invention provides a system for detecting surface defects of a metal plate based on a deep convolution neural network. The system can be used for detecting the surface defects of a metal plate in real time. The system comprises a camera, a mounting frame used for mounting the camera and a processor module, wherein the mounting frame stretches across a conveyer belt on an industrial site; the camera is arranged on the mounting frame and is used for collecting a surface image carried by the metal plate on the conveyer belt in real time; the processor module is used for adopting a deep convolution neural network algorithm for recognizing the defects of the surface image carried by the metal plate collected in real time. The system provided by the invention is suitable for the technical field of machine vision.

System for detecting surface defects of metal plate based on deep convolution neural 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 provides a vehicle exterior part image segmentation method based on deep learning. The vehicle exterior part image segmentation method includes that an original image is segmented and demarcated manually according to the segmentation target in the image, and the segmented labels are obtained; the original image and the segmented labels are input into a deep learning neural network, and the deep learning neural network is trained through the combination with a regression model to obtain a deep learning network model, wherein the deep learning neural network comprises a coding layer, a decoding layer and a convolution layer which are connected in order; and the image to be segmented is input into the deep learning network model for segmentation to obtain a segmented image. The neural network model comprises a coding layer, a decoding layer and a convolution layer which are connected in order, and compared with other nerve network structures, the neural network model deletes a full-connection layer and is more suitable for the segmentation of large and flat vehicle exterior part images. Meanwhile, the method does not require priori conditions for initialization, is high in accuracy and is not limited by the priori conditions.

Vehicle exterior part image segmentation method based on deep learning

The invention relates to a double-dynamic blurred image restoration method. The double-dynamic blurred image restoration method comprises the following steps of: obtaining a double-dynamic blurred image y, establishing an image pyramid of the double-dynamic blurred image, and establishing a motion blurred kernel and an out-of-focus blurred kernel of the double-dynamic blurred image y according to the image pyramid, wherein the resolution ratio of the blurred image in the image pyramid is sequentially increased from the top layer to the bottom layer; calculating optimal values of the motion blurred kernel, the out-of-focus blurred kernel and a clear image in a current layer of the image pyramid; and, according to the calculated optimal values of the motion blurred kernel, the out-of-focus blurred kernel and the clear image of the current image layer, calculating optimal values of the motion blurred kernel, the out-of-focus blurred kernel and the clear image of a next image layer till the clear image in the bottom layer of the image pyramid is calculated. By means of the method provided in the embodiment of the invention, the clear image is estimated in a process of continuously optimizing the motion blurred kernel and the out-of-focus blurred kernel; and thus, the clear image has enough definition.

Double-dynamic blurred image restoration method

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Vehicle exterior part image segmentation method based on deep learning

Double-dynamic blurred image restoration method