Detecting the object with external occlusion has always been a hot topic in computer version, while its accuracy is always limited due to the loss of original object information and increase of new occlusion noise. In this paper, we propose a occluded object detection algorithm named GC-FRCN (Generative feature completing Faster RCNN), which consists of the OSGM (Occlusion Sample Generation Module) and OSIM (Occlusion Sample Inpainting Module). Specifically, the OSGM mines and discards the feature points with high category response on the feature map to enhance the richness of occlusion scenes in the training data set. OSIM learns an implicit mapping relationship from occluded feature map to real feature map adversarially, which aims at improving feature quality by repair the noisy object feature. Extensive experiments and ablation studies have been conducted on four different datasets. All the experiments demonstrate the GC-FRCN can effectively detect objects with local external occlusion and has good robustness for occlusion at different scales.

检测具有外部遮挡的物体一直是计算机版本的热门话题，而由于原始物体信息的丢失和新的遮挡噪声的增加，其准确性总是受到限制。在本文中，我们提出了一种名为 GC-FRCN（生成特征完成 Faster RCNN）的遮挡物体检测算法，它由 OSGM（遮挡样本生成模块）和 OSIM（遮挡样本修复模块）组成。具体来说，OSGM 在特征图上挖掘和丢弃具有高类别响应的特征点，以增强训练数据集中遮挡场景的丰富度。OSIM 对抗性地学习从遮挡特征图到真实特征图的隐式映射关系，旨在通过修复嘈杂的对象特征来提高特征质量。已经在四个不同的数据集上进行了广泛的实验和消融研究。所有的实验都表明 GC-FRCN 可以有效地检测具有局部外部遮挡的对象，并且对不同尺度的遮挡具有良好的鲁棒性。