基于改进YOLOv9的禁垦陡坡地违规耕种区遥感影像检测方法  

Detecting illegal cultivation of step slopes from remote sensing images using improved YOLOv9

作　　者：吴仪邦 陈喆 李喆[1,2] 向大享 崔长露[1,2] WU Yibang;CHEN Zhe;LI Zhe;XIANG Daxiang;CUI Changlu(Spatial Information Technology Application Department,Changjiang River Scientific Research Institute,Wuhan 430010,China;Water Center for Intelligent Drainage Engineering Technology Research,Wuhan 430010,China)

机构地区：[1]长江科学院空间信息技术应用研究所,武汉430010 [2]武汉市智慧流域工程技术研究中心,武汉430010

出　　处：《农业工程学报》2024年第17期197-204,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：湖北省自然科学基金项目(2022CFD173);国家重点研发计划项目(2021YFC3000205)。

摘　　要：禁止开垦陡坡地范围内的违规耕种和开垦活动是水土保持法明令禁止的,针对目前对于违规耕种区的监管靠人工目视导致的检测效率低、时间成本高昂等问题,该研究设计一种基于改进YOLOv9的禁垦陡坡地违规耕种区遥感影像检测模型。首先,通过在YOLOv9的骨干网络中引入轻量级自注意力机制SimAM,验证获取全局信息和更丰富上下文信息在违规耕种区检测的作用;之后,将原有基于内核的动态上采样算子替换为超轻量化上采样算子DySample,以减少网络的参数量,提高识别速度和精度。消融试验结果表明,与原始模型相比,改进模型在权重大小、帧率基本一致的情况下,准确率、召回率、平均精度均值和F1得分分别提高了3.62、3.78、1.86、3.70个百分点。经过实地调查和遥感影像迁移试验,改进YOLOv9模型的平均识别精度为82.27%,优于FasterRCNN、YOLOv7、YOLOv8等主流目标检测算法,进一步验证了模型的可靠性和有效性,研究结果可为水土保持监管提供高质量数据支撑,对区域水土流失治理、生态环境保护及绿色低碳可持续发展具有重要决策意义。Illegal cultivation and reclamation activities are expressly prohibited in the area of reclamation of steep slopes.Large areas of sloping farmland have posed a high risk of soil erosion to reduce land productivity,even seriously threatening food security.However,manual ground surveys have been confined to investigating the illegal cultivation on the steep slopes and the prohibition of reclamation,due mainly to time-consuming and laborious.Meanwhile,the steep slopes are often distributed in dangerous areas,such as mountains and hills,leading to delays in the manual supervision of regional soil and water conservation.Fortunately,remote sensing interpretation and detection can be applied to monitor soil and water conservation in steep slopes,particularly with the rapid development of remote sensing and unmanned aerial vehicle(UAV)technology in recent years.Deep learning has been used to extract sloping farmland in the field of cultivated land identification.However,it is still lacking to consider the terrain slope,which has a great impact on soil erosion.At the same time,the large-scale remote sensing images often contain both concentrated contiguous fields and scattered small and micro fields;There are quite different directions and shapes of the fields,due to the differences in topography and landform,leading to the missed,false detection and low confidence in the detection of convolutional neural networks.The low detection efficiency and high time cost can also be caused by manual visual inspection in the supervision of illegal cultivation areas.In this study,a new model was designed to detect the remote sensing images from the illegal cultivation areas on steep slopes using the improved YOLOv9.The datasets were used from the GF-1 satellite and ALOS PALSAR.Firstly,12.5 m resolution DEM was calculated to extract the potential slope farmland areas using ALOS data.Then the GF-1 satellite remote sensing images in the potential slope farmland area were utilized as experimental data.The specific procedures and main con

关 键 词：遥感 深度学习 禁垦陡坡地 YOLOv9 注意力机制 动态上采样 

分 类 号：S26[农业科学—农业水土工程]