基于超深掩蔽与改进YOLOv8的不同成熟度番茄计数方法  

Counting tomatoes with different maturities using ultra-depth masking and improved YOLOv8

作　　者：张润池 周云成[1] 侯玉涵 刘泽钰 赵鸿舸 赵昱涵 ZHANG Runchi;ZHOU Yuncheng;HOU Yuhan;LIU Zeyu;ZHAO Hongge;ZHAO Yuhan(College of Information and Electrical Engineering,Shenyang Agricultural University,Shenyang 110866,China)

机构地区：[1]沈阳农业大学信息与电气工程学院,沈阳110866

出　　处：《农业工程学报》2024年第24期146-156,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划资助项目(2023YFD1501303,2021YFD1500204)。

摘　　要：针对在温室生产条件和作物种植模式约束下,番茄果实目标连续稳定跟踪困难,难以满足统计计数精度要求等问题,提出一种基于超深掩蔽与改进YOLOv8的不同成熟度果实计数方法。在YOLOv8基础上,构建融合全局特征的空间异质卷积核,优化设计卷积算子及目标检测网络,引入对果实目标标注更具鲁棒性的损失函数。提出用深度估计模型预测深度信息,动态生成深度阈值,基于该阈值,掩蔽远景果实目标,解决目标跟踪不稳定产生的计数精度低的问题。结果表明,与YOLOv8n相比,改进模型平均检测精度提高了3.2个百分点,召回率提高了3.7个百分点;将所设计的卷积算子用于目标检测模型,与使用该算子前相比,果实检测精度提高了2.7个百分点,与引入鲁棒性损失函数前相比,引入该损失后,检测精度提高了1.1个百分点;与不用超深掩蔽处理相比,应用该处理后,番茄果实计数精度提高了12.63个百分点;该方法的番茄果实计数精度为93.80%,对不同成熟度果实的计数精度不低于91.00%,计算速度为23帧/s。对YOLOv8的改进是有效的,超深掩蔽对提高番茄计数精度具有重要作用,研究可为基于视觉技术的果蔬产量统计提供技术参考。Continuously and stably tracking fruit objects is required for tomato production in modern agriculture in recent years.Some difficulties also remained on the statistical counting accuracy under greenhouse production,due to the mode constraints of crop planting.In this study,a counting method was proposed for tomatoes of varying maturity,utilizing ultra-depth masking and an improved YOLOv8 model.Multi-head self-attention(MHSA)was introduced to construct a spatially heterogeneous convolution kernel.The global features were also integrated using the lightweight convolution operator Involution.A new convolution operator was optimized and designed,termed Global Attention-based Involution(GAInvolution).This operator formed the backbone network of the tomato detector,YOLOT,an improved YOLOv8 model.The model also incorporated the WIoU(wise intersection over union)loss to improve the robustness of the object labeling process.In addition,the depth information was predicted by the mono-depth estimation model,called Depth Anything.The distant fruit objects were dynamically filtered to avoid object tracking loss or duplicate tracking.This processing was referred to as ultra-depth masking.A tomato counter was also optimized using the BoT-SORT algorithm.Combining the tomato detector,depth estimation model,ultra-depth masking,and object counter,the comprehensive framework was constructed to identify and count tomatoes of different maturity levels.The experimental results showed that the mean average precision at IoU thresholds of 0.5(mAP50)of the improved tomato detector increased by 3.2 percentage points,and the recall rate increased by 3.7 percentage points,indicating the effectiveness of the improved YOLOv8 model,compared with the original.The GAInvolution convolution operator significantly improved the tomato object detector.The detector with GAInvolution as the main operator achieved the 2.7 and 2.8 percentage point increase in the mAP50 and the recall rate,respectively,thereby significantly improving tomato detection perfo

关 键 词：番茄 果实计数 目标检测 多目标追踪 超深掩蔽 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] S224[自动化与计算机技术—控制科学与工程]