基于机器视觉的松林天牛远程智能监测系统
S763.38%S24; 松墨天牛和褐梗天牛是松树上两种重要蛀干害虫,及时获取松林天牛的数量变化趋势是松林害虫精准防治的重要前提.为此,该研究构建一款基于机器视觉的松林天牛远程智能监测系统.系统主要由诱捕器模块、天牛检测模块和系统Web端三部分组成.诱捕器模块通常放置于松林重点区域来诱捕天牛害虫,并通过摄像头定时采集天牛图像;天牛检测模型部署于边缘端,以深度学习YOLOv5s模型为基础搭建轻量化检测模型,实现边缘端的天牛实时检测统计;检测结果经无线传输在系统Web端进行呈现,实现天牛数据可追溯.试验结果表明,智能监测系统对天牛监测效果良好,模型的准确率为94.4％,召回率为93.6％,IoU阈...
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| Published in | 农业工程学报 Vol. 39; no. 17; pp. 190 - 198 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
山东农业大学信息科学与工程学院,泰安 271018%山东农业大学植物保护学院,泰安 271018%新泰市国有土门林场,新泰 271200%新泰市林业保护发展中心,新泰 271200%泰山林业有害生物防治检疫站,泰安 271018
01.09.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.202306060 |
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| Abstract | S763.38%S24; 松墨天牛和褐梗天牛是松树上两种重要蛀干害虫,及时获取松林天牛的数量变化趋势是松林害虫精准防治的重要前提.为此,该研究构建一款基于机器视觉的松林天牛远程智能监测系统.系统主要由诱捕器模块、天牛检测模块和系统Web端三部分组成.诱捕器模块通常放置于松林重点区域来诱捕天牛害虫,并通过摄像头定时采集天牛图像;天牛检测模型部署于边缘端,以深度学习YOLOv5s模型为基础搭建轻量化检测模型,实现边缘端的天牛实时检测统计;检测结果经无线传输在系统Web端进行呈现,实现天牛数据可追溯.试验结果表明,智能监测系统对天牛监测效果良好,模型的准确率为94.4％,召回率为93.6％,IoU阈值为0.5下的平均精度均值(mAP0.5)为96.2％,单张推理耗时为1.40 s,模型大小为9.3 MB;用户可通过系统Web端查看天牛数量变化趋势.该系统可实现诱捕器场景下的天牛远程智能监测,对提高森林害虫防控智能化水平具有重要意义. |
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| AbstractList | S763.38%S24; 松墨天牛和褐梗天牛是松树上两种重要蛀干害虫,及时获取松林天牛的数量变化趋势是松林害虫精准防治的重要前提.为此,该研究构建一款基于机器视觉的松林天牛远程智能监测系统.系统主要由诱捕器模块、天牛检测模块和系统Web端三部分组成.诱捕器模块通常放置于松林重点区域来诱捕天牛害虫,并通过摄像头定时采集天牛图像;天牛检测模型部署于边缘端,以深度学习YOLOv5s模型为基础搭建轻量化检测模型,实现边缘端的天牛实时检测统计;检测结果经无线传输在系统Web端进行呈现,实现天牛数据可追溯.试验结果表明,智能监测系统对天牛监测效果良好,模型的准确率为94.4％,召回率为93.6％,IoU阈值为0.5下的平均精度均值(mAP0.5)为96.2％,单张推理耗时为1.40 s,模型大小为9.3 MB;用户可通过系统Web端查看天牛数量变化趋势.该系统可实现诱捕器场景下的天牛远程智能监测,对提高森林害虫防控智能化水平具有重要意义. |
| Abstract_FL | Monochamus alternatus and Arhopalus rusticus are two important trunk-destroying pests on pine trees.Timely acquisition of their changing trends is required to precisely prevent and control of insect pests in pine forests.In this study,a remote intelligent monitoring system was constructed using machine vision,including the trapping module,the beetle detection,and the system web end.The trapping module was usually placed in the key areas of pine forests to capture the longicorn beetles,and then the images of the beetles were timely collected by cameras.The lightweight detection model(GMW-YOLOv5s)was deployed to recognize and count the longhorn beetles at the edge using the deep learning YOLOv5s model.The detection data was presented on the web end via the wireless transmission for the better traceability of beetles'distribution.The improved YOLOv5s model was used in the detection module to detect the different categories of longhorn beetles.The specific procedures were as follows.Firstly,the Ghost module was selected as the YOLOv5s backbone network to reduce the number of model parameters,and then a lightweight network was constructed.Secondly,the multi-scale detection mechanism was introduced into the neck network for the dependency relationship between the deep semantics and the shallow semantics multi-scale detection information.The feature layer weights of the shallow network were benefited to increase the detection capability of the tiny targets.Finally,the regression loss function of WIoU(wise intersection over union)bounding box was introduced to optimize the target for the high localization accuracy of longhorn beetles.The experimental results show that the better performance was achieved in the intelligent monitoring system.Ghost module was introduced into the detection module to reduce the model size by 6.9 MB and the parameter number by 47.6％.The multi-scale detection was improved the precision and recall by 0.7％and 0.4％,respectively.The mAP0.5 increased to 96.4％with the introduction of WIoU loss function.The GMW-YOLOv5s model was integrated with three improvement methods to detect the longhorn beetles.The precision and recall rate of the improved model were 94.4％and 93.6％,respectively.The mean average precision reached 96.2％under IoU=0.5(mAP0.5),which was higher than that of the original.The improved model shared the better image feature learning and object detection,indicating the superior performance in the detection of small objects.The comparison was also implemented with many mainstream target detection models,including Faster-RCNN,SSD,YOLOX,YOLOv7-tiny,YOLOv5n,and YOLOv5s.The indicators of GMW-YOLOv5s model were improved,such as the precision,recall and mAP0.5.An accurate detection was achieved in the position of longhorn beetles,especially in the densely distributed scenes.In addition,the improved model was used to effectively distinguish some targets,and then avoid missed the detections.A single inference time of 1.40 s and a model size of 9.3 MB were suitable for the deployment on the edge devices with limited computational resources,which was fully met the accuracy and intelligence requirements of the longicorn beetle detection task.Furthermore,the mean average precision and recall were 99.0％,and 98.7％in the two types of longicorn beetles counting respectively.The model and manual counts were relatively close to each other.There was the low number of omissions and misdetections,indicating the less error in counting the longicorn beetles of the image.The monitoring system can be expected to regularly and automatically collect the beetle images on a regular basis,and then accurately detect and count them at the edge end.The trend of the number of longhorn beetles can also be viewed through the system web interface.Therefore,it is of great significance for the remote intelligent monitoring of longhorn beetles in the trap scenarios,particularly for the higher intelligent level of forest pest control. |
| Author | 季英超 王亚琪 常希忠 孙丰刚 王建丽 陈龙 兰鹏 赵吉建 栾巧巧 |
| AuthorAffiliation | 山东农业大学信息科学与工程学院,泰安 271018%山东农业大学植物保护学院,泰安 271018%新泰市国有土门林场,新泰 271200%新泰市林业保护发展中心,新泰 271200%泰山林业有害生物防治检疫站,泰安 271018 |
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| Author_FL | LUAN Qiaoqiao SUN Fenggang CHEN Long CHANG Xizhong WANG Yaqi LAN Peng WANG Jianli ZHAO Jijian JI Yingchao |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DOI | 10.11975/j.issn.1002-6819.202306060 |
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| DocumentTitle_FL | Remote intelligent monitoring system for pine forest longicorn beetles based on machine vision |
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| Keywords | YOLOv5 automatic counting edge computing monitoring intelligent 智能 machine vision 机器视觉 监测 边缘计算 longicorn beetles 自动计数 天牛 |
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| Title | 基于机器视觉的松林天牛远程智能监测系统 |
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