Lightweight Object Detection Model Based on Cross Channel Fusion and Attention Mechanism
In view of the high complexity of the current object detection algorithm model and the slow speed of real-time testing pictures,a lightweight target detection model based on cross channel fusion and attention mechanism is proposed,which is convenient for carrying mobile scenes.Firstly,MV1 module is used to build the backbone feature extraction network,CDC module is used to transform multi-layer basic convolution,and a lightweight model based on Depth Separable Convolution(DW)is proposed.Secondly,in the neck of the network,three channels are extended to four channels;Meanwhile,a cross channel path is designed to fuse deep and shallow information to further enhance feature extraction.Finally,an Efficient Channel Attention(ECA)is integrated to make the algorithm focus on the target position in the image.The simulation results show that the average recognition accuracy of all categories of the algorithm in Pascal VOC 07+12 dataset reaches 91.64%.Compared with the latest YOLOv7,the model parameters are reduced by 60%,and the detection speed reaches 55.54 FPS,the effectiveness of the proposed algorithm is proved.
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