A Recurrent Reinforcement Learning Approach for Small Object Detection with Dynamic Refinement

Small object detection is one of the challenging tasks in recent computer vision researches. The low resolution and noisy representation are internal reasons associated with small object detection. To better address this task, we propose a novel reinforcement learning approach with dynamic refinement. Specifically, we design a Recurrent Reinforcement Module (RRM) to iteratively learn contextual information and improve semantic label dependency and the image-label relevance. Besides, the Dynamic Refinement Module (DRM) is designed to dynamically adjust the attentive regions that are related to small objects. To evaluate the effectiveness of our proposed approach, we design extensive experiments and comparisons on four datasets (i.e. PASCAL VOC, MS COCO, Google AVA and UA-DETRAC). The comprehensive experiment results demonstrate that our approach has the superiority of small object detection for both images and videos.