Lightweight Non-Destructive Detection of Diseased Apples Based on Structural Re-Parameterization Technique

Abstract

This study addresses the challenges in the non-destructive detection of diseased apples, specifically the high complexity and poor real-time performance of the classification model for detecting diseased fruits in apple grading. Research is conducted on a lightweight model for apple defect recognition, and an improved VEW-YOLOv8n method is proposed. The backbone network incorporates a lightweight, re-parameterization VanillaC2f module, reducing both complexity and the number of parameters, and it employs an extended activation function to enhance the model’s nonlinear expression capability. In the neck network, an Efficient-Neck lightweight structure, developed using the lightweight modules and augmented with a channel shuffling strategy, decreases the computational load while ensuring comprehensive feature information fusion. The model’s robustness and generalization ability are further enhanced by employing the WIoU bounding box loss function, evaluating the quality of anchor frames using outlier metrics, and incorporating a dynamically updated gradient gain assignment strategy. Experimental results indicate that the improved model surpasses the YOLOv8n model, achieving a 2.7% increase in average accuracy, a 24.3% reduction in parameters, a 28.0% decrease in computational volume, and an 8.5% improvement in inference speed. This technology offers a novel, effective method for the non-destructive detection of diseased fruits in apple grading working procedures.