福州大学学报（自然科学版）2024，Vol.52Issue(4)：421-429,9.DOI:10.7631/issn.1000-2243.23252
改进Mask R-CNN的车辆检测算法
Improved algorithm of Mask R-CNN for vehicle detection
摘要
Abstract
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%.关键词
车辆检测/Mask R-CNN算法/PSA极自注意力机制/ECA注意力机制/Soft-NMS算法Key words
vehicle detection/Mask R-CNN algorithm/PSA mechanism/ECA mechanism/Soft-NMS algorithm分类
信息技术与安全科学引用本文复制引用
汪菊,孙玉,吴宜良..改进Mask R-CNN的车辆检测算法[J].福州大学学报（自然科学版）,2024,52(4):421-429,9.基金项目
国家自然科学基金资助项目(42171426) （42171426）
