Abstract: The present disclosure provides a method for detecting a defect of a film. The method includes obtaining a film image, determining one or more pieces of scratch information corresponding to the film image through processing the film image using a recognition model, the recognition model includes a convolution layer, a regression layer, and a classification layer, determining whether each piece of scratch information in the one or more pieces of scratch information meets a preset condition, each piece of scratch information includes position information, angle information, and size information, in response to a determination that each piece of scratch information meets the preset condition, adding one or more pieces of annotation information to the one or more pieces of scratch information that meets the preset condition, and generating prompt information based on the one or more pieces of annotation information.

Abstract: An end-to-end multimodal gait recognition method based on deep learning includes: first extracting gait appearance features (color, texture and the like) through RGB video frames, and obtaining a mask by semantic segmentation of the RGB video frames; then extracting gait mask features (contour and the like) through the mask; and finally performing fusion and recognition on the two kinds of features. The method is configured for extracting gait appearance feature and mask feature by improving GaitSet, improving semantic segmentation speed on the premise of ensuring accuracy through simplified FCN, and fusing the gait appearance feature and the mask feature to obtain a more complete information representation.

Abstract: The present disclosure provides a method for detecting a defect of a film. The method includes obtaining a film image, determining one or more pieces of scratch information corresponding to the film image through processing the film image using a recognition model, the recognition model includes a convolution layer, a regression layer, and a classification layer, determining whether each piece of scratch information in the one or more pieces of scratch information meets a preset condition, each piece of scratch information includes position information, angle information, and size information, in response to a determination that each piece of scratch information meets the preset condition, adding one or more pieces of annotation information to the one or more pieces of scratch information that meets the preset condition, and generating prompt information based on the one or more pieces of annotation information.

Abstract: An end-to-end multimodal gait recognition method based on deep learning includes: first extracting gait appearance features (color, texture and the like) through RGB video frames, and obtaining a mask by semantic segmentation of the RGB video frames; then extracting gait mask features (contour and the like) through the mask; and finally performing fusion and recognition on the two kinds of features. The method is configured for extracting gait appearance feature and mask feature by improving GaitSet, improving semantic segmentation speed on the premise of ensuring accuracy through simplified FCN, and fusing the gait appearance feature and the mask feature to obtain a more complete information representation.