%0 Journal Article %A YANG Shan %A WANG Jian %A HU Li %A LIU Bo %A ZHAO Hao %T Research on Occluded Object Detection by Improved RetinaNet %D 2022 %R 10.3778/j.issn.1002-8331.2107-0277 %J Computer Engineering and Applications %P 209-214 %V 58 %N 11 %X Aiming at the problem of low detection accuracy caused by the dense and overlapping instances, a detection framework with the improved regression loss function and dynamic non-maximum suppression(NMS) is proposed in this paper. Rep-GIoU-Loss derived from the combination of GIoU-Loss and the rejection factor Rep is used for location regression of object, which increases the correlation among regression parameters and reduces the probability of candidate bounding box offset to adjacent truth boxes. As a result, Rep-GIoU-Loss not only improves the regression accuracy of object location effectively, but also enjoys good robustness to the occlusion problem. In addition, a density prediction branch for predicting occlusion degree is added. The computed value from the density prediction branch is utilized as the dynamic threshold of the NMS, which reduces the missed and false detection instances. The detection accuracy of the proposed approach is improved by 1.3 percentage points on Pascal VOC and 2.8 percentage points on the self-made dataset. The experimental results show the effectiveness of the proposed method. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2107-0277