关键词: 无锚框, 多样性感受野, 注意力机制, 特征融合, 目标检测

Abstract: 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 method can improve PASCAL VOC by 1.4 points and obtain a mAP of 42.8 on MS COCO. The detection performance is significantly better than many advanced algorithms.  

Key words: anchor-free, diverse receptive field, attention mechanism, feature fusion, object detection