NMS-Free Oriented Object Detection Based on Channel Expansion and Dynamic Label Assignment in UAV Aerial Images

Abstract

Object detection in unmanned aerial vehicle (UAV) aerial images has received extensive attention in recent years. The current mainstream oriented object detection methods for aerial images often suffer from complex network structures, slow inference speeds, and difficulties in deployment. In this paper, we propose a fast and easy-to-deploy oriented detector for UAV aerial images. First, we design a re-parameterization channel expansion network (RE-Net), which enhances the feature representation capabilities of the network based on the channel expansion structure and efficient layer aggregation network structure. During inference, RE-Net can be equivalently converted to a more streamlined structure, reducing parameters and computational costs. Next, we propose DynamicOTA to adjust the sampling area and the number of positive samples dynamically, which solves the problem of insufficient positive samples in the early stages of training. DynamicOTA improves detector performance and facilitates training convergence. Finally, we introduce a sample selection module (SSM) to achieve NMS-free object detection, simplifying the deployment of our detector on embedded devices. Extensive experiments on the DOTA and HRSC2016 datasets demonstrate the superiority of the proposed approach.

Keywords

• UAV aerial image
• oriented object detection
• label assignment
• embedded device