An Anchor-Free Lightweight Object Detection Network

Abstract

Existing anchor-free object detection methods have achieved some amazing results, but these methods are relatively complex and the inference speed is also slow. In this paper, an anchor-free lightweight object detection network is proposed. The proposed method effectively overcomes the limitation of detection model by anchor-free mechanism, and the lightweight backbone network reduces the computational cost. In addition, the proposed small object enhancement module can enhance the focus on small objects, which improves the detection capability of small objects. Besides, a label assignment strategy is proposed to determine the prominent feature, and a center correction mechanism is introduced to make the predicted bounding box closer to the ground truth to further improve the detection accuracy. Extensive experiments are conducted on MS COCO and Pascal VOC datasets, and the results demonstrate that the proposed method achieves better results than the existing detection methods on detection accuracy by increasing 0.5% on the MS COCO dataset, and has a detection accuracy increase of 0.4% on the Pascal VOC dataset, which proves the superiority of the proposed method.