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1. 江西省自然资源事业发展中心
2. 南昌大学先进制造学院
Published:2025
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[1]徐世亮,赖民权,雷雨,等.基于改进YOLOv5s的小目标工程车辆定点监测识别算法[J].新疆大学学报(自然科学版中英文),2025,42(01):99-106.
[1]徐世亮,赖民权,雷雨,等.基于改进YOLOv5s的小目标工程车辆定点监测识别算法[J].新疆大学学报(自然科学版中英文),2025,42(01):99-106. DOI: 10.13568/j.cnki.651094.651316.2024.04.28.0001.
DOI:10.13568/j.cnki.651094.651316.2024.04.28.0001.
为有效减少并预防因违法土地开垦和矿产挖掘而造成自然环境破坏的行为,利用部署到高塔上的摄像头,提出了一种在复杂环境中进行各类工程车辆检测的ETS-YOLO小目标监测识别算法.首先,使用EfficientViT网络替换YOLOv5s的主干特征提取网络,以提高注意力多样性,大幅缩减模型参数量.其次,增加小目标检测层,增强网络对浅层语义信息的提取,以提高小目标检测效果.最后,使用软非极大值抑制算法(soft-NMS)替换原有NMS函数,以有效识别遮挡、重叠目标.实验结果表明:改进后的模型平均准确度均值(m AP)为93.3%、参数量为5.90 M、检测速度为52 f/s.相较YOLOv5s模型,m AP提升2.6%,参数量下降16.1%.
In order to effectively reduce and prevent the damage to the natural environment caused by illegal land reclamation and mineral excavation
an ETS-YOLO small target monitoring and recognition algorithm for the detection of various types of engineering vehicles in complex environments is proposed using cameras deployed to high towers. Firstly
the EfficientViT network is used to replace the backbone feature extraction network of YOLOv5s in order to improve the attention diversity and significantly reduce the number of model parameters.Secondly
a small target detection layer is added to enhance the network's extraction of shallow semantic information to improve the performance of small target detection. Finally
the original NMS function is replaced with the soft non-maximal suppression algorithm(soft-NMS) to effectively recognize occluded and overlapped targets. The experimental results show that the improved model has a mean average precision(m AP) of 93.3%
a parameter count of 5.90 M
and a detection speed of 52 f/s. Compared with the YOLOv5s model
the m AP is improved by2.6% and the parameter count is decreased by 16.1%.
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