计算机工程与应用2024，Vol.60Issue(9)：142-150,9.DOI:10.3778/j.issn.1002-8331.2312-0043
面向无人机视角下小目标检测的YOLOv8s改进模型
Improved YOLOv8s Model for Small Object Detection from Perspective of Drones
摘要
Abstract
Facing with the problems of small and densely distributed image targets,uneven class distribution,and model size limitation of hardware conditions,object detection from the perspective of drones has less precise results.A new improved model based on YOLOv8s with multiple attention mechanisms is proposed.To solve the problem of shared attention weight parameters in receptive field features and enhance feature extraction ability,receptive field attention convolution and CBAM(concentration based attention module)attention mechanism are introduced into the backbone,adding atten-tion weight in channel and spatial dimensions.By introducing large separable kernel attention into feature pyramid pool-ing layers,information fusion between different levels of features is increased.The feature layers with rich semantic infor-mation of small targets are added to improve the neck structure.The inner-IoU loss function is used to improve the MPDIoU(minimum point distance based IoU)function and the inner-MPDIoU instead of the original loss function is used to enhance the learning ability for difficult samples.The experimental results show that the improved YOLOv8s model has improved mAP,P,and R by 16.1%,9.3%,and 14.9%respectively on the VisDrone dataset,surpassing YOLOv8m in per-formance and can be effectively applied to unmanned aerial vehicle visual detection tasks.关键词
无人机/小目标检测/YOLOv8s/感受野注意力/大型可分离卷积Key words
unmanned aerial vehicle(UAV)/small object detection/YOLOv8s/receptive field attention/large separable kernel attention分类
信息技术与安全科学引用本文复制引用
潘玮,韦超,钱春雨,杨哲..面向无人机视角下小目标检测的YOLOv8s改进模型[J].计算机工程与应用,2024,60(9):142-150,9.基金项目
教育部产学合作协同育人项目(220606363154256) （220606363154256）
国家级大学生创新创业训练计划项目(202210285042Z). （202210285042Z）
