An over-regression suppression method to discriminate occluded objects of same category

Occlusion is a key challenge in object detection. It is hard to discriminate objects accurately when they gather together and occlude each other, especially when they belong to same category which easily leads to the problem that multiple objects are regressed into the same bounding box. To address this problem, an over-regression suppression (ORS) method is proposed to take full advantage of supervised information. Firstly, annotated information is utilized to compute the overlaps between different ground truth boxes. Then, the regression loss function is redesigned by adding a penalty term which is associated with the aforementioned overlaps to prevent Over-regression. Finally, the validity of the algorithm is proved by making some changes in Faster R-CNN, in which a k-means ++ clustering algorithm is used to automatically generate various size anchors by learning the shape regularities of objects from dataset, and the Soft-NMS, a nearly cost-free method, is introduced to replace the traditional NMS. Extensive evaluations on the challenging PASCAL VOC and MS COCO benchmarks demonstrate the superiority of ORS in handling intra-class occlusion. Its performance increases when dataset contains more large objects and hard samples, as demonstrated by the results on the MS COCO dataset.