In the past decade, deep learning based visual object detection has received a significant amount of attention, but cases when heavy intra-class occlusions occur are not studied thoroughly. In this work, we propose a novel Non-MaximumSuppression (NMS) algorithm that dramatically improves the detection recall while maintaining high precision in scenes with heavy occlusions. Our NMS algorithm is derived from a novel embedding mechanism, in which the semantic and geometric features of the detected boxes are jointly exploited. The embedding makes it possible to determine whether two heavily-overlapping boxes belong to the same object in the physical world. Our approach is particularly useful for car detection and pedestrian detection in urban scenes where occlusions tend to happen. We validate our approach on two widely-adopted datasets, KITTI and CityPersons, and achieve state-of-the-art performance.

本文提出一种名为SG-Det的模型，使用一种新颖的嵌入机制来联合利用检测框的语义和几何特征，从而在面对类内遮挡较多的场景时显著提高了检测召回率，特别适用于城市场景的汽车和行人检测，在KITTI和CityPersons数据集上展示了SG-Det模型的最优性能。

学习分离：检测城市场景中的严重遮挡物体