YOLO-Drone: A Scale-Aware Detector for Drone Vision

Abstract

Object detection is an important task in drone vision. Since the number of objects and their scales always vary greatly in the drone-captured video, small object-oriented feature becomes the bottleneck of model performance, and most existing object detectors tend to underperform in drone-vision scenes. To solve these problems, we propose a novel detector named YOLO-Drone. In the proposed detector, the backbone of YOLO is firstly replaced with ConvNeXt, which is the state-of-the-art one to extract more discriminative features. Then, a novel scale-aware attention (SAA) module is designed in detection head to solve the large disparity scale problem. A scale-sensitive loss (SSL) is also introduced to put more emphasis on object scale to enhance the discriminative ability of the proposed detector. Experimental results on the latest VisDrone 2022 test-challenge dataset (detection track) show that our detector can achieve average precision (AP) of 39.43%, which is tied with the previous state-of-the-art, meanwhile, reducing 39.8% of the computational cost.