Object detection via learning occluded features based on generative adversarial networks

Object detection is a fundamental task in computer vision. There often exist occlusions between objects in real life, which result in that some features of an object are missing, and detection accuracy is reduced. Therefore, we propose a generative adversarial network for learning occluded features (GANLOF). It is divided into two parts: the generator of occluded features and the discriminator. Firstly, we generate random occlusions for pictures in datasets, and the occluded pictures are the inputs of the network. Then we use the generator to restore pooling features in occluded regions, and the occluded pooling features and the un-occluded image pooling features are distinguished by the discriminator. Meanwhile, we use the detection loss to supervise the generator, so that the recovered occluded features are more accurate. The proposed GANLOF can be used as a component added into two-phase object detection networks. Compared with the Faster RCNN and other models, the mean average precision (mAP) of model is improved on the PASCAL VOC2007 dataset and the KITTI dataset. Copyright ©2021 Control and Decision.