刘建航,何鉴恒,陈海华,王晓政,翟海滨.基于改进YOLO v4和ICNet的番茄串检测模型[J].农业机械学报,2023,54(10):216-224,254.
LIU Jianhang,HE Jianheng,CHEN Haihua,WANG Xiaozheng,ZHAI Haibin.Development of Detection Model for Tomato Clusters Based on Improved YOLO v4 and ICNet[J].Transactions of the Chinese Society for Agricultural Machinery,2023,54(10):216-224,254.
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| 基于改进YOLO v4和ICNet的番茄串检测模型 [下载全文] |
| Development of Detection Model for Tomato Clusters Based on Improved YOLO v4 and ICNet [Download Pdf][in English] |
| 投稿时间：2022-09-01 |
| DOI：10.6041/j.issn.1000-1298.2023.10.021 |
| 中文关键词: 番茄串 采摘机器人 深度学习 YOLO v4 ICNet 采摘模型 |
| 基金项目:山东省自然科学基金项目（ZR2020MF005） |
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| 中文摘要:针对深层神经网络模型部署到番茄串采摘机器人，存在运行速度慢，对目标识别率低，定位不准确等问题，本文提出并验证了一种高效的番茄串检测模型。模型由目标检测与语义分割两部分组成。目标检测负责提取番茄串所在的矩形区域，利用语义分割算法在感兴趣区域内获取番茄茎位置。在番茄检测模块，设计了一种基于深度卷积结构的主干网络，在实现模型参数稀疏性的同时提高目标的识别精度，采用K-means++聚类算法获得先验框，并改进了DIoU距离计算公式，进而获得更为紧凑的轻量级检测模型(DC-YOLO v4)。在番茄茎语义分割模块（ICNet）中以MobileNetv2为主干网络，减少参数计算量，提高模型运算速度。将采摘模型部署在番茄串采摘机器人上进行验证。采用自制番茄数据集进行测试，结果表明，DC-YOLO v4对番茄及番茄串的平均检测精度为99.31%，比YOLO v4提高2.04个百分点。语义分割模块的mIoU为81.63%，mPA为91.87%，比传统ICNet的mIoU提高2.19个百分点，mPA提高1.47个百分点。对番茄串的准确采摘率为84.8%，完成一次采摘作业耗时约6s。 |
| LIU Jianhang HE Jianheng CHEN Haihua WANG Xiaozheng ZHAI Haibin |
China University of Petroleum (East China)
;Institute of Computing Technology, Chinese Academy of Sciences; National Computer Network Emergency Response Technical Team/Coordination Center of China |
| Key Words:tomato clusters picking robot deep learning YOLO v4 ICNet picking model |
| Abstract:For the deep neural network model deployed to embedded devices (such as tomato clusters picking robots), there are some problems, such as slow running speed, low recognition rate of picking targets, inaccurate positioning and so on, an efficient model for tomato clusters detection was proposed and verified. The model was composed of two modules: detection and semantic segmentation. Target detection was responsible for extracting the rectangular region where the tomato cluster was located, and then using the semantic segmentation algorithm to obtain the tomato stem position in the rectangular region. In the tomato detection module, a backbone network based on deep convolution structure was designed to improve the accuracy of crop recognition while realizing the sparsity of model parameters. K-means++ clustering algorithm was used to obtain a priori frame, and DIoU distance calculation formula was improved to obtain a more compact lightweight detection model (DC-YOLO v4). In the semantic segmentation module (ICNet), MobileNetv2 was used as the backbone network to reduce the amount of parameter calculation and improve the operation speed of the model. The model was deployed on the tomato clusters picking robot for verification. The self-made tomato data set was used for testing. The results showed that the average detection accuracy was 99.31% on tomato test set, outperforming YOLO v4 by 2.04 percentage points. The mIoU and mPA achieved 81.63% and 91.87% on tomato stem set, exceeding ICNet by 2.19 percentage points and 1.47 percentage points, respectively. The accurate picking rate of tomato clusters was 84.8%, it took 6s to complete a picking operation. |
Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.
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