Optimizing Small Object Detection in UAV Images with Precise Feature Enhancement

Unmanned aerial vehicles (UAV) are extensively utilized in various applications due to their compact size and flexibility. However, the detection task in UAV images faces significant challenges stemming from the abundance of small targets and the wide range of target sizes. To address this issue, we propose an object detection method specifically designed for UAV images, incorporating two enhancements to the strong baseline model YOLOX, which excels at detecting small and multi-scale targets. First, we introduce a high-resolution feature map to preserve detailed information crucial for small targets. We then introduce an attention mechanism to guide the model to focus more on small targets in high-dimensional features. Experimental results on the VisDrone-VID2019 dataset confirm the effectiveness and superiority of our proposed method.