华南农业大学学报2026，Vol.47Issue(1)：94-105,12.DOI:10.7671/j.issn.1001-411X.202505006
基于改进YOLOv7算法的自然环境下柑橘缺陷检测
Detection of citrus defects in natural environment based on improved YOLOv7 algorithm
摘要
Abstract
[Objective]Citrus defect recognition is a key link in realizing automatic citrus fruit picking and controlling fruit quality.This study aims to improve the accuracy of citrus defect recognition in natural environments and achieve all-weather operation of intelligent picking.[Method]By optimizing key modules,an improved YOLOv7 algorithm was proposed.The specific improvements included introducing the complete intersection over union(CIoU)loss function to improve bounding box regression accuracy;adopting the HardSwish activation function to enhance network learning and computational efficiency;integrating the attention free transformer(AFT)to strengthen target feature recognition;combining the residual multilayer perceptron(ResMLP)and dynamic convolution(DC)technologies to improve model's adaptability and stability under complex lighting conditions.[Result]Using a dual light source system,this algorithm achieved all-weather detection of citrus fruits and their defects in natural environments.It detected defects such as black spots and cracks under natural light or white light,while at night,violet light served as a complementary means to detect defects that were not obvious under white light or natural light based on fluorescent responses.The experimental results showed that the improved YOLOv7 algorithm achieved 97.9%recognition accuracy for citrus fruits and 92.8%for defects during daytime,which were 3.8 and 13.4 percentage points higher than those of the original YOLOv7 algorithm,respectively;the defect recognition accuracy at night reached 82.4%.[Conclusion]The citrus defect recognition method proposed in this paper has high accuracy and a wide applicable time range,providing new insights for the intelligent harvesting in the citrus industry.关键词
机器视觉/目标检测/柑橘缺陷/YOLOv7/缺陷检测Key words
Machine vision/Object detection/Citrus defect/YOLOv7/Defect detection分类
农业科技引用本文复制引用
余林豪,钟沅,宋淑然,熊俊涛,孙道宗,薛秀云,代秋芳,李震..基于改进YOLOv7算法的自然环境下柑橘缺陷检测[J].华南农业大学学报,2026,47(1):94-105,12.基金项目
国家自然科学基金(32472020) （32472020）
广东省现代农业产业技术体系创新团队建设项目(2024CXTD10) （2024CXTD10）
广东省重点领域研发计划(2023B0202090001) （2023B0202090001）
财政部和农业农村部:国家现代农业产业技术体系建设专项(CARS-26) （CARS-26）
