Enhancing Sustainable Traffic Monitoring: Leveraging NanoSight–YOLO for Precision Detection of Micro-Vehicle Targets in Satellite Imagery

Abstract

Satellite remote sensing technology significantly aids road traffic monitoring through its broad observational scope and data richness. However, accurately detecting micro-vehicle targets in satellite imagery is challenging due to complex backgrounds and limited semantic information hindering traditional object detection models. To overcome these issues, this paper presents the NanoSight–YOLO model, a specialized adaptation of YOLOv8, to boost micro-vehicle detection. This model features an advanced feature extraction network, incorporates a transformer-based attention mechanism to emphasize critical features, and improves the loss function and BBox regression for enhanced accuracy. A unique micro-target detection layer tailored for satellite imagery granularity is also introduced. Empirical evaluations show improvements of 12.4% in precision and 11.5% in both recall and mean average precision (mAP) in standard tests. Further validation of the DOTA dataset highlights the model’s adaptability and generalization across various satellite scenarios, with increases of 3.6% in precision, 6.5% in recall, and 4.3% in mAP. These enhancements confirm NanoSight–YOLO’s efficacy in complex satellite imaging environments, representing a significant leap in satellite-based traffic monitoring.