SSD Target Detection Algorithm with Channel Attention Mechanism
摘要
In order to improve the accuracy and robustness of the original Single Shot Multibox Detector(SSD) algorithm for small target detection,this paper proposes a SSD target detection algorithm based on channel attention mechanism.The algorithm implements global pooling on high-level feature maps on the basis of the original SSD algorithm.Then the semantic information of high-level feature maps is enhanced by using the channel attention mechanism,and the dilated convolution structure is introduced for subsampling of low-level feature maps to enlarge their receptive field,so as to improve details and location information.Finally,different levels of feature maps are fused by cascading to implement effective recognition of small objects and occluded objects.Experimental results on the PASCAL VOC dataset show that compared with the original SSD algorithm,the proposed algorithm improves the mean Average Precision(mAP) by 2.2% with a higher accuracy and robustness for small object detection.