Object detection via feature fusion based single network
Jian Li, Jianjun Qian, Jian Yang · 2017
This paper presents a novel network, coined single unified fully convolutional network (SingleNet), for object detection. The proposed method mainly combines two ideas: (1) Our approach aggregates hierarchical features and map them into a uniform space. So, it can further enhance the feature representation ability to degrade the recognition error; (2) To approximate the ground-truth box, we design a set of dense boxes over different aspect ratios and scales per feature map pixel to regress bounding box. It's easy to improve the location performance using dense boxes scheme. Additionally, SingleNet is convenient to train and can be integrated into detection system. Experimental results (mAP: 0.776) on VOC 2007 test demonstrate the advantages of the proposed method over state-of-the-art methods.