关键词: 目标跟踪, YOLOv5s, DeepSORT, 注意力机制, 特征融合网络

Abstract: The study conducts focus on enhancing the detection accuracy of the YOLOv5s algorithm within the DeepSORT framework. The research work encompasses the integration of the attention mechanism called Convolutional Block Attention Module （CBAM） into the network structure of YOLOv5s， the refinement of the bidirectional feature fusion network Bi-directional Feature Pyramid Network （BiFPN）， and the adoption of Enhanced Intersection over Union （EIoU） as the bounding box loss function. Test results obtained from the VOC 2007 pedestrian dataset indicates improvements when compared to the original algorithm. Specifically， the proposed algorithm exhibits an increase of 0.3 percentage points in precision， 1.0 percentage points in recall， and 0.3 percentage points in average precision. Subsequently， the algorithm is evaluated on the MOT17 dataset， showcasing significant enhancements in multiple metrics. The MOTA metric experiences a 1.8 percentage points improvement， while IDF1， MT， and IDR see enhancements of 2.9 percentage points， 1， and 2.7， respectively. Moreover， the number of false negatives （FN） decreases by 4373， and the number of mostly lost targets （ML） decreases by 11. Overall， these empirical findings substantiate the efficacy of the improved YOLOv5s algorithm as a detector， effectively augmenting tracking precision in various scenarios.

Key words:  , target tracking, YOLOv5s, DeepSORT, attention mechanism, feature fusion network