Construction and Application of a Feature Extraction Module for Small Target Prohibited Items in Security Inspection Images
Aiming at the problem that small target prohibited items in logistics package security inspection images is easy to miss detection,a feature extraction module suitable for small target prohibited items detection is constructed by introducing a convolutional attention module on the multi-branch parallel network of the receptive field module.On this basis,the constructed feature extraction module is integrated into the backbone of the YOLOv5 model,so that the model focuses on the important features of the image in the process of prohibited items detection.In order to fully utilize the feature extraction ability of the constructed module for small target objects,a spatial depth conversion module is used to replace the downsampling module in the original model,so that the YOLOv5 model can retain the feature information of small target objects as much as possible during the feature extraction process,and improve the detection effect for small target prohibited items.
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