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.

中文翻译：

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一种过度回归抑制方法来区分相同类别的被遮挡对象

遮挡是物体检测中的关键挑战。当对象聚集在一起并且相互遮挡时，尤其是当它们属于同一类别时，很难准确地区分对象，这很容易导致多个对象回归到同一边界框中的问题。为了解决这个问题，提出了一种过度回归抑制（ORS）方法来充分利用监督信息。首先，带注释的信息用于计算不同地面真值框之间的重叠。然后，回归损失函数是通过将其与上述的重叠，以防止相关联的惩罚项重新设计的过-回归。最后，通过在Faster R-CNN中进行一些更改来证明该算法的有效性，其中使用k-means ++聚类算法通过从数据集中学习对象的形状规律性以及使用Soft -引入了一种几乎免费的方法-NMS，以取代传统的NMS。对具有挑战性的PASCAL VOC和MS COCO基准进行了广泛的评估，证明了ORS在处理组内咬合方面的优势。如MS COCO数据集上的结果所示，当数据集包含更多大对象和硬样本时，其性能会提高。