Walnut Surface Defect Classification and Detection Model Based on Enhanced YOLO11n

Xinyi Ma, Zhongjia Hao (), Shuangyin Liu () and Jingbin Li
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Xinyi Ma: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Zhongjia Hao: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Shuangyin Liu: College of Artificial Intelligence, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Jingbin Li: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China

Agriculture, 2025, vol. 15, issue 15, 1-16

Abstract: Aiming at the challenges in practical production lines, including the difficulty in accurately capturing external defects on continuously rolling walnuts, distinguishing subtle defects, and differentiating narrow fissures from natural walnut textures, this paper proposes an improved walnut external defect detection model named YOLO11-GME, based on YOLO11n. Firstly, the original backbone network is replaced with the lightweight GhostNetV1 network, enhancing model precision while meeting real-time detection speed requirements. Secondly, a Mixed Local Channel Attention (MLCA) mechanism is incorporated into the neck to strengthen the network’s ability to capture features of subtle defects, thereby improving defect recognition accuracy. Finally, the EIoU loss function is adopted to enhance the model’s localization capability for irregularly shaped defects and reduce false detection rates by improving the scale sensitivity of bounding box regression. Experimental results demonstrate that the improved YOLO11-GME model achieves a mean Average Precision (mAP) of 96.2%, representing improvements of 8.6%, 7%, and 5.8% compared to YOLOv5n, YOLOv8n, and YOLOv10n, respectively, and a 5.9% improvement over the original YOLOv11. Precision rates for the normal, fissure, and inferior categories increased by 8.7%, 5.3%, and 3.7%, respectively. The frame rate remains at 43.92 FPS, approaching the original model’s 51.02 FPS. These results validate that the YOLO11-GME model enhances walnut external defect detection accuracy while maintaining real-time detection speed, providing robust technical support for defect detection and classification in industrial walnut production.

Keywords: walnut surface defects; YOLO11; attention mechanism; image classification; object detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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