Target Detection Algorithm Based on Global Feature Fusion in Parallel Dual Path Backbone

The active downsampling of the backbone of conventional single path architecture often leads to insufficient feature extraction and information loss. At the same time, simply adding or splicing feature pyramids is not conducive to the integration of shallow to deep features. To solve these problems, a target detection algorithm based on global feature fusion in parallel dual path backbone is proposed. Firstly, the dual path architecture backbone is used to extract spatial and semantic information in parallel, and the dual path fusion module is used to promote the mutual complement between feature information. Secondly, the top feature is added to the pyramid pooled multi-scale pool mapping at the same time, and the attention mechanism is used to gather the multi-scale pooled features, so as to further improve the multi-scale detection performance. Then, the global scale information is gathered, which is integrated into different layers of features by using self-attention mechanism, and repeated many times to construct the neck network structure of global feature fusion, which effectively improves the ability of neck network to fuse global context information. Finally, the head adopts Ghost Conv combined with channel shuffling operation to maintain model performance and reduce parameter redundancy. Experiments on KITTI, BDD100K and PASCAL VOC datasets show that the average accuracy of the proposed algorithm is improved by 3.5, 3.4 and 2.7 percentage points compared with the baseline model (YOLOv7-tiny), respectively. Experimental results show that the proposed algorithm improves the detection performance in complex scenes, and has low requirements for computing power and other resources.