Feature Fusion and Adversary Occlusion Networks for Object Detection

Current object detection techniques have difficulties in detecting small objects and a low level of accuracy in detecting occluded objects. To solve these problems, this paper proposes an object detection framework named FFAN which is based on Faster R-CNN that introduces a feature fusion network and an adversary occlusion network into the structure. The feature fusion network combines a feature map of low resolution and high semantic information with a feature map of high resolution and low semantic information using the deconvolution operation to increase the ability to extract low-level features in the network. FFAN then generates a single advanced feature map with high resolution and high semantic information which is used to predict the detection of small objects in the image more effectively. The adversary occlusion network creates occlusion on a deep feature map of the object, and generates an adversary training sample that is difficult for the detector to discriminate. At the same time, the detector classifies accurately the generated occluded adversary samples by self-learning. The two compete with and learn from each other to further improve the performance of the algorithm. We train FFAN on the PASCAL VOC 2007, PASCAL VOC 2012, MS COCO and KITTI datasets. A number of quantitative and qualitative experiments show that FFAN achieves a state-of-the-art detection accuracy.