LAM-YOLO: Drones-based Small Object Detection on Lighting-Occlusion Attention Mechanism YOLO

• URL: http://arxiv.org/abs/2411.00485v1
• Date: Fri, 01 Nov 2024 10:00:48 GMT
• Title: LAM-YOLO: Drones-based Small Object Detection on Lighting-Occlusion Attention Mechanism YOLO
• Authors: Yuchen Zheng, Yuxin Jing, Jufeng Zhao, Guangmang Cui,
• Abstract summary: LAM-YOLO is an object detection model specifically designed for drone-based images. We introduce a light-occlusion attention mechanism to enhance the visibility of small targets under different lighting conditions. Second, we utilize an improved SIB-IoU as the regression loss function to accelerate model convergence and enhance localization accuracy.
• Score: 0.9062164411594178
• License:
• Abstract: Drone-based target detection presents inherent challenges, such as the high density and overlap of targets in drone-based images, as well as the blurriness of targets under varying lighting conditions, which complicates identification. Traditional methods often struggle to recognize numerous densely packed small targets under complex background. To address these challenges, we propose LAM-YOLO, an object detection model specifically designed for drone-based. First, we introduce a light-occlusion attention mechanism to enhance the visibility of small targets under different lighting conditions. Meanwhile, we incroporate incorporate Involution modules to improve interaction among feature layers. Second, we utilize an improved SIB-IoU as the regression loss function to accelerate model convergence and enhance localization accuracy. Finally, we implement a novel detection strategy that introduces two auxiliary detection heads for identifying smaller-scale targets.Our quantitative results demonstrate that LAM-YOLO outperforms methods such as Faster R-CNN, YOLOv9, and YOLOv10 in terms of mAP@0.5 and mAP@0.5:0.95 on the VisDrone2019 public dataset. Compared to the original YOLOv8, the average precision increases by 7.1\%. Additionally, the proposed SIB-IoU loss function shows improved faster convergence speed during training and improved average precision over the traditional loss function.

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