YOLOv10-DSNet: A Lightweight and Efficient UAV-Based Detection Framework for Real-Time Small Target Monitoring in Smart Cities

The effective management of smart cities relies on real-time data from urban environments, where Unmanned Aerial Vehicles (UAVs) are critical sensing platforms. However, deploying high-performance detection models on resource-constrained UAVs presents a major challenge, particularly for identifying small, dense targets like pedestrians and vehicles from high altitudes. This study aims to develop a lightweight yet accurate detection algorithm to bridge this gap. We propose YOLOv10-DSNet, an improved architecture based on YOLOv10. The model integrates three key innovations: a parallel dual attention mechanism (CBAM-P) to enhance focus on small-target features; a novel lightweight feature extraction module (C2f-LW) to reduce model complexity; and an additional 160 × 160 detection layer to improve sensitivity to fine-grained details. Experimental results demonstrate that YOLOv10-DSNet significantly outperforms the baseline, increasing mAP50-95 by 4.1% while concurrently decreasing computational costs by 1.6 G FLOPs and model size by 0.7 M parameters. The proposed model provides a practical and powerful solution that balances high accuracy with efficiency, advancing the capability of UAVs for critical smart city applications such as real-time traffic monitoring and public safety surveillance.