针对无人机航拍时拍摄的对象大小不一、种类繁杂且容易被建筑遮挡等问题,提出了一种基于YOLOv5s的无人机目标检测改进算法VA-YOLO。在已有的主干网络中添加CA注意力机制模块,扩大检测区域,获得更准确的位置信息;针对检测小目标时尺度不...针对无人机航拍时拍摄的对象大小不一、种类繁杂且容易被建筑遮挡等问题,提出了一种基于YOLOv5s的无人机目标检测改进算法VA-YOLO。在已有的主干网络中添加CA注意力机制模块,扩大检测区域,获得更准确的位置信息;针对检测小目标时尺度不一导致语义丢失的问题,添加小目标检测层与BiFPN结构,加深浅层语义与深层语义结合,以此丰富对检测目标的语义信息;使用损失函数Varifocal loss与EIoU,改善模型对小目标检测的准确性。实验结果表明,在VisDrone2019-DET数据集上,该算法的平均检测精度(mean Average Precision,mAP)达到了39.01%,相比YOLOv5s提高了6.26%。展开更多
Accurate cattle body detection can significantly enhance the efficiency and quality of animal husbandry production.Traditional manual observation approaches are not only inefficient but also lack objectivity,while com...Accurate cattle body detection can significantly enhance the efficiency and quality of animal husbandry production.Traditional manual observation approaches are not only inefficient but also lack objectivity,while computer vision-based methods demand prolonged training periods and present challenges in implementation.To address these issues,this paper develops a novel precise cattle body detection solution,namely YOLOv5-VF-W3.By introducing the Varifocal loss,the YOLOv5-VF-W3 model can handle imbalanced samples and focus more attention on difficult-to-recognize instances.Additionally,the introduction of the WIoUv3 loss function provides the model with a wise gradient gain allocation strategy.This strategy reduces the competitiveness of high-quality anchor boxes while mitigating harmful gradients produced by low-quality anchor boxes,thereby emphasizing anchor boxes of ordinary quality.Through these enhancements,the YOLOv5-VF-W3 model can accurately detect cattle bodies,improving the efficiency and quality of animal husbandry production.Numerous experimental results have demonstrated that the proposed YOLOv5-VF-W3 model achieves superior cattle body detection results in both quantitative and qualitative evaluation criteria.Specifically,the YOLOv5-VF-W3 model achieves an mAP of 95.2%in cattle body detection,with individual cattle detection,leg detection,and head detection reaching 95.3%,94.8%,and 95.4%,respectively.Furthermore,in complex scenarios,especially when dealing with small targets and occlusions,the model can accurately and efficiently detect individual cattle and key body parts.This brings new opportunities for the development of precision livestock farming.展开更多
文摘针对无人机航拍时拍摄的对象大小不一、种类繁杂且容易被建筑遮挡等问题,提出了一种基于YOLOv5s的无人机目标检测改进算法VA-YOLO。在已有的主干网络中添加CA注意力机制模块,扩大检测区域,获得更准确的位置信息;针对检测小目标时尺度不一导致语义丢失的问题,添加小目标检测层与BiFPN结构,加深浅层语义与深层语义结合,以此丰富对检测目标的语义信息;使用损失函数Varifocal loss与EIoU,改善模型对小目标检测的准确性。实验结果表明,在VisDrone2019-DET数据集上,该算法的平均检测精度(mean Average Precision,mAP)达到了39.01%,相比YOLOv5s提高了6.26%。
基金supported by the Shanxi Province Basic Research Program(Grant No.202203021212444)the GuangHe Fund D(Grant No.ghfund202407042032)+5 种基金Shanxi Agricultural University Science and Technology Innovation Enhancement Project(Grant No.CXGC2023045)Shanxi Postgraduate Education and Teaching Reform Project Fund(Grant No.2022YJJG094)Shanxi Agricultural University Doctoral Research Start-up Project(Grant No.2021BQ88)Shanxi Agricultural University Academic Restoration Research Project(Grant No.2020xshf38)Young and Middle-aged Top-notch Innovative Talent Cultivation Program of the Software College,Shanxi Agricultural University(Grant No.SXAUKY2024005)the Key Research and Development Program of Zhejiang Province under Grand(Grant No.2024C01104,2024001026).
文摘Accurate cattle body detection can significantly enhance the efficiency and quality of animal husbandry production.Traditional manual observation approaches are not only inefficient but also lack objectivity,while computer vision-based methods demand prolonged training periods and present challenges in implementation.To address these issues,this paper develops a novel precise cattle body detection solution,namely YOLOv5-VF-W3.By introducing the Varifocal loss,the YOLOv5-VF-W3 model can handle imbalanced samples and focus more attention on difficult-to-recognize instances.Additionally,the introduction of the WIoUv3 loss function provides the model with a wise gradient gain allocation strategy.This strategy reduces the competitiveness of high-quality anchor boxes while mitigating harmful gradients produced by low-quality anchor boxes,thereby emphasizing anchor boxes of ordinary quality.Through these enhancements,the YOLOv5-VF-W3 model can accurately detect cattle bodies,improving the efficiency and quality of animal husbandry production.Numerous experimental results have demonstrated that the proposed YOLOv5-VF-W3 model achieves superior cattle body detection results in both quantitative and qualitative evaluation criteria.Specifically,the YOLOv5-VF-W3 model achieves an mAP of 95.2%in cattle body detection,with individual cattle detection,leg detection,and head detection reaching 95.3%,94.8%,and 95.4%,respectively.Furthermore,in complex scenarios,especially when dealing with small targets and occlusions,the model can accurately and efficiently detect individual cattle and key body parts.This brings new opportunities for the development of precision livestock farming.
