Improved algorithm of Mask R-CNN for vehicle detection
To enhance the vehicle detection performance in complex scenarios,an improved vehicle detection algorithm based on Mask R-CNN is proposed in this paper.In the backbone network,the improved vehicle detection algorithm introduce the polarized self-attention(PSA)mechanism to enhance the feature extraction capability of the ResNet50 network.Additionally,in the top-level network of the feature pyramid network add a branch with an efficient channel attention(ECA)mechanism for feature fusion with the original branch.This helps alleviate information loss caused by channel reduction in the top-level features.The design of the convolutional detection head is also revamped to achieve more accurate bounding box regression.Furthermore,the cosine annealing algorithm and Soft-NMS algorithm to optimize the training process and post-processing results.Experimenta l results demonstrate that the improved Mask R-CN N vehicle detection algorithm outperforms the original Mask R-CNN algorithm in complex scenarios.On the CNRPark-EXT test set,it achieves an average precision improvement of 3.8%.On the more challenging MiniPark test set,the average precision is improved by 7.9%.
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