TY - JOUR AU - Huang, Yecheng AU - Chen, Jiaxin AU - Huang, Di PY - 2022/06/28 Y2 - 2024/03/28 TI - UFPMP-Det:Toward Accurate and Efficient Object Detection on Drone Imagery JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 1 SE - AAAI Technical Track on Computer Vision I DO - 10.1609/aaai.v36i1.19986 UR - https://ojs.aaai.org/index.php/AAAI/article/view/19986 SP - 1026-1033 AB - This paper proposes a novel approach to object detection on drone imagery, namely Multi-Proxy Detection Network with Unified Foreground Packing (UFPMP-Det). To deal with the numerous instances of very small scales, different from the common solution that divides the high-resolution input image into quite a number of chips with low foreground ratios to perform detection on them each, the Unified Foreground Packing (UFP) module is designed, where the sub-regions given by a coarse detector are initially merged through clustering to suppress background and the resulting ones are subsequently packed into a mosaic for a single inference, thus significantly reducing overall time cost. Furthermore, to address the more serious confusion between inter-class similarities and intra-class variations of instances, which deteriorates detection performance but is rarely discussed, the Multi-Proxy Detection Network (MP-Det) is presented to model object distributions in a fine-grained manner by employing multiple proxy learning, and the proxies are enforced to be diverse by minimizing a Bag-of-Instance-Words (BoIW) guided optimal transport loss. By such means, UFPMP-Det largely promotes both the detection accuracy and efficiency. Extensive experiments are carried out on the widely used VisDrone and UAVDT datasets, and UFPMP-Det reports new state-of-the-art scores at a much higher speed, highlighting its advantages. The code is available at https://github.com/PuAnysh/UFPMP-Det. ER -