基于改进YOLO v8n网络的番茄成熟度实时检测算法  

Improved YOLO v8n Network for Real-time Detection of Tomato Maturity

作　　者：任晶秋[1] 万恩晗 单蜜 张光华[1] 卢为党[2] REN Jingqiu;WAN Enhan;SHAN Mi;ZHANG Guanghua;LU Weidang(School of Electrical and Information Engineering,Northeast Petroleum University,Daqing 163318,China;College of Information Engineering,Zhejiang University of Technology,Hangzhou 310014,China)

机构地区：[1]东北石油大学电气信息工程学院,大庆163318 [2]浙江工业大学信息工程学院,杭州310014

出　　处：《农业机械学报》2025年第3期374-382,450,共10页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金项目(62271447)。

摘　　要：为应对番茄采摘面临的果农老龄化、劳动力短缺和人工成本上涨等挑战,解决在复杂果园环境下番茄采摘机器人视觉系统成熟度检测精度低和实例分割不准确等问题,本文提出一种基于改进YOLO v8n网络的番茄成熟度实时检测算法。首先,通过在YOLO v8n网络中引入通道嵌入位置注意力模块和改进大核卷积块注意力模块,能够在浅层网络保留番茄目标位置信息,建立目标区域之间的长距离依赖关系,从而增加YOLO v8n网络对显著番茄特征的关注。然后,在LaboroTomato数据集上进行了对比实验,改进YOLO v8n相较于原YOLO v8n网络,检测和分割的mAP@50和mAP@50-95分别提高0.4、1.4个百分点和0.3、1.2个百分点。最后,实现了改进YOLO v8n网络在低成本、低算力和低功耗的Jetson Nano平台上的轻量化部署,模型内存占用量由满溢减少到2.4 GB,推理速度加倍。该研究可为番茄采摘机器人在复杂场景下实时、准确检测番茄成熟度提供技术支撑。To address the numerous challenges faced in tomato harvesting,such as the aging of farmers,labor shortages,and rising labor costs,and resolve issues related to the low maturity detection accuracy and inaccurate instance segmentation of tomato harvesting robots in complex orchard environments,an improved YOLO v8 network-based real-time tomato maturity detection algorithm was proposed.Firstly,by introducing the channel embedded positional attention module and an improved large kernel convolutional block attention module into the YOLO v8n network,the algorithm can effectively retain the positional information of tomato targets in the shallow network layers and establish long-range dependencies between target regions,thereby significantly increasing the attention of the YOLO v8n network to critical tomato features.Then a series of comprehensive and rigorous comparative experiments were conducted on the LaboroTomato dataset,demonstrating that the improved YOLO v8n network achieved 0.4 percentage points,1.4 percentage points,and 0.3 percentage points,1.2 percentage points improvements in detection and segmentation mAP@50 and mAP@50-95,respectively,compared with that of the original YOLO v8n network.Finally,the improved YOLO v8n network was lightweight deployed on the low-cost,low-computation,and low-power Jetson Nano platform,successfully reducing memory usage from overflow to 2.4 GB and doubling the inference speed.The research result can provide robust technical support for the real-time and accurate detection of tomato maturity by tomato harvesting robots in complex scenarios,significantly enhancing the overall efficiency and effectiveness of automated tomato harvesting operations.

关 键 词：番茄成熟度 目标检测 YOLO v8n 注意力模块 轻量化 Jetson Nano 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] S24[自动化与计算机技术—计算机科学与技术]