基于改进YOLOv7算法的自然环境下柑橘缺陷检测OA

[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.