基于无人机视频影像的油菜苗检测与计数
S252; 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...
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| Published in | 农业工程学报 Vol. 40; no. 10; pp. 147 - 156 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
华中农业大学工学院,武汉 430070
01.05.2024
农业农村部长江中下游农业装备重点实验室,武汉 430070%华中农业大学工学院,武汉 430070 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.202312224 |
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| Abstract | S252; 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 R-CNN、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. |
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| AbstractList | S252; 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 R-CNN、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. |
| Abstract_FL | 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 alleviate the negative impact of invalid targets in the image background.3)The improved YOLOv5s was achieved in the AP50 and AP95 scores of 93.1％and 67.5％,respectively.Among them,AP50 was significantly higher by 14.82,26.37,and 3.3 percentage points,respectively,while AP95 was higher by 25.7,33.9,and 6.7 percentage points,respectively,compared with the classical target detection,such as Faster R-CNN,SSD,and YOLOX.The counting trial of rapeseed seedlings demonstrated that the counting model achieved the maximum precision of 96.34％with an average of 93.75％.Furthermore,the rapeseed counting efficiency exceeded the well-trained operator with an average increase of 9.52 times.In the case of online real-time counting of rapeseed seedlings,the maximum difference in counting precision was 1.87％on the UAV counting platform under different weather conditions.The excellent generalization,counting precision,and efficiency fully met the real-time requirements for rapeseed seedlings.The finding can provide a powerful reference to assess the quality of rapeseed seeding and field management. |
| Author | 张维 邱天 朱耀宗 徐世兴 李文成 黄小毛 |
| AuthorAffiliation | 华中农业大学工学院,武汉 430070;农业农村部长江中下游农业装备重点实验室,武汉 430070%华中农业大学工学院,武汉 430070 |
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| Author_FL | XU Shixing QIU Tian LI Wencheng ZHANG Wei HUANG Xiaomao ZHU Yaozong |
| Author_FL_xml | – sequence: 1 fullname: HUANG Xiaomao – sequence: 2 fullname: ZHANG Wei – sequence: 3 fullname: QIU Tian – sequence: 4 fullname: ZHU Yaozong – sequence: 5 fullname: XU Shixing – sequence: 6 fullname: LI Wencheng |
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| DocumentTitle_FL | Rapeseed seedling detection and counting based on UAV videos |
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| Keywords | YOLO deep learning online real-time counting 在线实时计数 DeepSORT 深度学习 无人机 UAV 油菜苗 offline video counting 离线视频计数 rapeseed seedling |
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| Publisher | 华中农业大学工学院,武汉 430070 农业农村部长江中下游农业装备重点实验室,武汉 430070%华中农业大学工学院,武汉 430070 |
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| Snippet | S252; 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... |
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| Title | 基于无人机视频影像的油菜苗检测与计数 |
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