Small object detection in aerial traffic imagery: A benchmark for motorbike-dominated road scenes

Abstract

Unmanned Aerial Vehicles (UAVs) have become indispensable for traffic monitoring, urban planning, and disaster management, particularly in high-density traffic environments like those in Southeast Asia. Vietnamese traffic, characterized by its high density of compact vehicles and unconventional patterns, poses unique challenges for object detection systems. Moreover, UAV imagery introduces additional complexities, such as variable object orientations and high-density scenes, which existing algorithms struggle to handle effectively. In this paper, we present two novel UAV datasets, UIT-Drone4 and UIT-Drone7 with 4 and 7 classes, respectively. These datasets encompass diverse environments, from urban traffic to rural roads and market areas, and provide detailed annotations for object orientation. We benchmark ten state-of-the-art object detection methods, including YOLOv8-v11 and orientation-specific approaches such as Oriented RepPoints, SASM, RTMDet, and Rotated Faster R-CNN, to evaluate their performance under real-world conditions. Our results reveal critical limitations in current methods when applied to motorbike-dominated traffic, highlighting challenges such as high object density, complex orientations, and varying environmental conditions. The UIT-Drone4 and UIT-Drone7 datasets are publicly available at UIT-Drone4-Link and UIT-Drone7-Link, respectively.