YOLO-CAM: A Lightweight UAV Object Detector with Combined Attention Mechanism for Small Targets

Object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging due to the prevalence of small targets, complex backgrounds, and the stringent requirement for real-time processing on computationally constrained platforms. Existing methods often struggle to balance detection accuracy, particularly for small objects, with operational efficiency. To address these challenges, this paper proposes YOLO-CAM, an enhanced object detector based on YOLOv5n. First, a novel Combined Attention Mechanism (CAM) is integrated to synergistically recalibrate features across both channel and spatial dimensions, enhancing the network’s focus on small targets while suppressing background clutter. Second, the detection head is strategically optimized by introducing a dedicated high-resolution head for tiny targets and removing a redundant head, thereby expanding the detectable size spectrum down to small pixels with reduced parameters. Finally, the CIoU loss is replaced with the inner-Focal-EIoU loss to improve bounding box regression accuracy, especially for low-quality examples and small objects. Extensive experiments on the challenging VisDrone2019 benchmark demonstrate the effectiveness of our method. YOLO-CAM achieves a mean Average Precision (mAP0.5) of 31.0%, which represents a significant 7.5% improvement over the baseline YOLOv5n, while maintaining a real-time inference speed of 128 frames per second. Comparative studies show that our approach achieves a superior balance between accuracy and efficiency compared to other state-of-the-art detectors. The results indicate that the proposed YOLO-CAM establishes a new way for accuracy–efficiency trade-offs in UAV-based detection. Due to its lightweight design and high performance, it is particularly suitable for deployment on resource-limited UAV platforms for applications requiring reliable real-time small object detection.