基于无人机视频影像的油菜苗检测与计数  

Rapeseed seedling detection and counting based on UAV videos

作　　者：黄小毛[1,2] 张维 邱天 朱耀宗 徐世兴 李文成 HUANG Xiaomao;ZHANG Wei;QIU Tian;ZHU Yaozong;XU Shixing;LI Wencheng(College of Engineering,Huazhong Agricultural University,Wuhan 430070,China;Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River,Ministry of Agriculture and Rural Affairs,Wuhan 430070,China)

机构地区：[1]华中农业大学工学院,武汉430070 [2]农业农村部长江中下游农业装备重点实验室,武汉430070

出　　处：《农业工程学报》2024年第10期147-156,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金资助项目(52075211);国家重点研发计划(2021YFD1600503);中央高校基本科研业务费专项(2662023GXPY002)。

摘　　要：针对油菜生长早期传统人工苗情调查方法效率低、主观意识强,不能满足大面积或经常性高精度苗期调查作业需求的问题,该研究基于无人机影像及机器学习技术,提出一种油菜苗视频流检测模型及计数方法。通过对YOLO系列基础模型添加多头自注意力,用BasicRFB(basic receptive field block)模块替换原有的空间池化结构(spatial pyramid pooling-fast,SPPF)模块,并对Neck部分添加一维卷积及更换下采样方式等,进一步结合DeepSORT(deep simple online and real-time tracking)算法和越线计数技术实现对油菜苗的数量统计。算例测试结果表明,改进后YOLOv5s的交并比阈值0.50的平均精度均值达到93.1%,交并比阈值0.50~0.95的平均精度均值达到了67.5%,明显优于Faster RCNN、SSD和YOLOX等其他经典目标检测算法,交并比阈值0.50的平均精度均值分别高出14.82、26.37和3.3个百分点,交并比阈值0.50~0.95的平均精度均值分别高出25.7、33.9和6.7个百分点。油菜苗计数试验结果表明,离线视频计数时,在合理的种植密度区间内,所提算法的油菜苗计数精度平均达到93.75%,平均计数效率为人工计数的9.54倍;在线实时计数时,在不同天气情况下,计数平台的油菜苗计数精度最大相差1.87个百分点,具有良好的泛化性,满足油菜苗计数实时性要求。Early growth of rapeseed can be used to assess the performance of seeder,grain yield,crop management,and fertilizer application.Rapid and accurate detection is critical to rapeseed production and yield.However,the manual seedling survey cannot fully meet the operational demands for the extensive or frequent high-precision seedling,due to the low efficiency and subjectivity.In this study,efficient detection and counting models were proposed for the video stream of rapeseed seedlings using unmanned aerial vehicle(UAV)imagery and machine learning.Multi-head self-attention was added to the YOLO series models.The attention was then reduced to irrelevant semantic information.The model was improved to focus on the target object.The basic receptive field block(BasicRFB)module was selected to replace the original spatial pyramid pooling-fast(SPPF)module.One-dimensional convolution was added to the Neck part.The downsampling was then changed to achieve the target of rapeseed seedlings in the image.The efficient fusion of features was also promoted to focus on the key features among other interference factors.The deep simple online and real-time tracking(DeepSORT)was further combined with the cross-line counting to achieve the continuous tracking and target number counting.In addition,the counting model was deployed on edge computing devices.A real-time target counting was designed using a multi-rotor UAV platform.The edge computing device was used to realize the real-time detection and counting of rape seedlings.The targets of rape seedlings were processed in the video stream that was captured by the camera in real time.The experimental results show that:1)The improved model with multi-head self-attention was significantly focused on the rape seedling area in the image.A better performance was achieved in extracting the target features than before.2)The detection accuracy of rapeseed seedlings was improved using BasicRFB and the operator of the Neck part.The detection misjudgment of targets was reduced to effectively allev

关 键 词：无人机 深度学习 油菜苗 离线视频计数 在线实时计数 YOLO DeepSORT 

分 类 号：S252[农业科学—农业机械化工程]