基于无锚框目标检测算法的多样性感受野注意力特征补偿.

As one of the research hotspots of object detection, anchor free abandons a large number of predefined box Settings and adopts pixel-by-pixel method for prediction. Even so, it does not deal well with overlapping objects. In addition, the ability of network to obtain global information of images is weak and receptive field mismatch is easy to occur. Therefore, this paper proposes two modules: diverse receptive field attention mechanism (DRAM) and global context-guided feature fusion module (GCF). Extensive experiments on the PASCAL VOC and MS COCO confirm the effectiveness of our method. Compared with the baseline FCOS, the proposed methodcanimprovePASCAL VOCby1.4 pointsandobtaina mAPof42.8on MSCOCO. The detection performanceis significantly beter than many advanced algorithms. [ABSTRACT FROM AUTHOR]