Aiming at a single feature is difficult to accurately describe the complexity and anisotropy of color and texture of colored spun fabric images, and the color and texture of images may affect each other. A colored spun fabric image retrieval method based on decoupled feature is proposed. The color templates and texture templates of the images are extracted and decoupled; at the same time, deep hash coding is used to calculate the similarity; finally, the preliminary retrieval results are reordered using the decoupled feature. In this paper, seven different types of colored spun fabric sample images are used for retrieval, and the Top-10 recall and mAP of this system reach 95.00% and 86.56%, respectively, which improves the recall and mAP of retrieving Top-10 compared to the methods without feature decoupling and those with feature decoupling only for a single feature.
A detail-aware multi-angle vehicle recognition algorithm is proposed to address the problem of detail information loss due to pooling operation in multi-angle vehicle recognition. Firstly, considering that the differences between different vehicles are concentrated in the vehicle length and axle regions, the mid-level features are selected to build a local perception module, and the ECA attention mechanism is embedded to enhance the network's discrimination of details in local regions and suppress the interference of low discrimination features; secondly, a void space pyramid pooling model is introduced to increase the perceptual field to aggregate multi-scale contextual information, which reduces the problem of detail information loss and improves the global The second is the introduction of the null space pyramid pooling model, which increases the perceptual field to aggregate multi-scale contextual information, reduces the loss of detail information and improves the perception of global features. The experimental results show that the detection accuracy mAP of the improved algorithm reaches 96.1%, and the proposed method can effectively obtain local features with discrimination and improve the perception ability of the global features.
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