基于特征融合和混合注意力的小目标检测OA北大核心CSTPCD

To address to the low feature information,low detection rates,and high false rate and missing rate in the target detection task,a Tr-SSD algorithm based on multiscale feature fusion and a hybrid attention mechanism was proposed.Firstly,a Resnet50 residual network was utilized as the backbone network for the SSD algorithm to en-hance its feature extraction capabilities.Secondly,a hybrid attention mechanism was designed and applied to the mid-scale feature maps of the network to enhance effective information within the feature maps and establish long-range dependencies between pieces of information.Finally,a FPN(feature pyramid network)structure was formed by using network layers centered around the Transformer instead of the original backbone network in the SSD algo-rithm,which fused feature information of different scales to more accurately locate small targets.Experimental re-sults showed that the Tr-SSD algorithm achieved mAP values of 81.9%,87.5%,and 88.4%on the PASCAL VOC dataset,HRSID dataset,and RSOD remote sensing dataset,respectively.This represented an improvement of 4.7 percentage points,6.8 percentage points,and 9.2 percentage points compared to the original SSD algorithm.Mo-reover,the detection speed could meet the requirements for real-time detection.