YOLO-SMUG: An Efficient and Lightweight Infrared Object Detection Model for Unmanned Aerial Vehicles

To tackle the high computational demands and accuracy limitations in UAV-based infrared object detection, this study proposes YOLO-SMUG, a lightweight detection algorithm optimized for small object identification. The model incorporates an enhanced backbone architecture that integrates the lightweight Shuffle_Block algorithm and the Multi-Scale Dilated Attention (MSDA) mechanism, enabling effective small object feature extraction while significantly reducing parameter size and computational cost without compromising detection accuracy. Additionally, a lightweight inverted bottleneck structure, C2f_UIB, along with the GhostConv module, replaces the conventional C2f and standard convolutional layers. This modification decreases computational complexity while maintaining the model’s ability to capture and integrate essential feature information across multiple scales. Furthermore, the standard CIoU loss is substituted with MPDIoU loss, improving object localization accuracy and enhancing overall positioning precision in infrared imagery. Experimental results on the HIT-UAV dataset, which consists of infrared imagery collected by UAV platforms, demonstrate that YOLO-SMUG outperforms the baseline YOLOv8s, achieving a 3.58% increase in accuracy, a 6.49% improvement in the F1-score, a 57.04% reduction in computational cost, and a 64.38% decrease in parameter count. These findings underscore the efficiency and effectiveness of YOLO-SMUG, making it a promising solution for UAV-based infrared small object detection in complex environments.