基于FCML-YOLO v8的杏鲍菇表征提取与分级方法

Characterization Extraction and Classification of Pleurotus eryngii Based on FCML-YOLO v8

摘要:

针对杏鲍菇分级依赖人工、效率低且主观性强的问题，提出了一种基于改进YOLO v8n-seg的杏鲍菇自动分级检测方法FCML-YOLO v8。基于FCML-YOLO v8模型，结合从属判断、掩模合并及中心筛选方法，实现了对杏鲍菇尺寸与病斑情况的准确评估；通过图像细化与拟合曲线技术，有效量化了杏鲍菇弯曲度；并利用CIE XYZ颜色空间值，分析了杏鲍菇颜色特征。综合以上4项指标，实现了杏鲍菇自动化分级检测。模型训练测试结果表明，FCML-YOLO v8模型预测框精确率、召回率和平均精度均值分别达到94.41%、88.48%和92.04%，而在掩膜检测方面，其精确率、召回率和平均精度均值分别为90.52%、84.14%和86.79%。与YOLO v8n-seg模型相比，预测框和掩膜检测平均精度均值分别提升6.39、4.79个百分点。杏鲍菇分级检测试验结果表明，FCML-YOLO v8模型平均精度均值高达92.98%，满足工业级应用需求。

Abstract:

Addressing the challenges of apricot mushroom grading, which relies heavily on manual labor, is inefficient, and prone to subjectivity. An automated grading and detection method for apricot mushrooms named FCML-YOLO v8 was proposed. Building upon the improved YOLO v8n-seg, the network sequentially integrated the feature-focused diffusion pyramid network (FDPN), characteristic attention fusion module (CAFM), and multi-level channel attention (MLCA) mechanism. By combining the FCML-YOLO v8 model with subordinate judgment, mask merging, and center screening methods, accurate assessments of apricot mushroom size and disease spot conditions was achieved. Additionally, through image refinement and curve-fitting techniques, it effectively quantified the curvature of apricot mushrooms. Furthermore, by leveraging CIE XYZ color space values, the color characteristics of apricot mushrooms were precisely analyzed. By integrating these four indicators, the automated grading and detection of apricot mushrooms were successfully implemented. Experimental results demonstrated that the FCML-YOLO v8 model achieved a precision of 94.41%, a recall rate of 88.48%, and an mAP@0.5 of 92.04% in bounding box prediction. In mask detection, it achieved a precision of 90.52%, a recall rate of 84.14%, and an mAP@0.5 of 86.79%. Compared with the original YOLO v8n-seg model, the mAP@0.5 of FCML-YOLO v8 was improved by 6.39 and 4.79 percentage points, respectively, in bounding box prediction and mask detection. In the constructed apricot mushroom grading and detection experimental setup, the FCML-YOLO v8 model achieved an mAP@0.5 of 92.98%, fully meeting the requirements of industrial-grade applications.