Find Small Objects in UAV Images by Feature Mining and Attention

Xiaolei Liu, Chengcai Leng, Xiaoming Niu, Zhao Pei, Irene Cheng, Anup Basu

Abstract: With the increasing popularity of unmanned aerial vehicles (UAVs), the accuracy of detecting small objects in large-view images is also expected to increase. However, accurate small object detection is still a challenging problem. Currently, image pyramid network, feature pyramid network (FPN), rich training strategies, and data augmentation are widely used to address this problem. To accurately detect small objects, the most important thing is to mine for more feature information. We propose widened residual block (WRB) to break through the bottleneck of residual information gain to extract more feature information. The second is to emphasize or suppress features to prevent small objects from being overwhelmed by a broad background. We introduce an attention mechanism into path aggregation network (PANet) and propose enhanced attention PANet (EA-PANet), which consists of two parts: context attention module (COAM) and attention enhancement module (AEM). The COAM outputs attention heat maps with context, and the AEM fuses features from the channel attention module (CAM) and COAM to avoid distraction from a vast background. In addition, we design a lightweight decoupled attention head (DA-head) to dynamically compute important regions for specific tasks and achieve reliable predictions. Experiments show that our method outperforms state-of-the-art (SOTA) detectors. The source code for this work is available at https://github.com/liuxiaolei111/FindSmallObjects .