Research on lemon quality grading detection based on improved YOLOv5s

Abstract

Objective: To address the lack of effective grading and processing methods in the current large-scale development of lemon cultivation in China, this study introduces a Class-guide feature extraction module on top of the existing YOLOv5s model. This further enhances the network's feature extraction capabilities and integrates the correlation between lemon detection and grading tasks. The study designs a multi-task learning algorithm that combines image-level and box-level fusion, and incorporates defect detection and quality grading into a single task model through joint training. Experimental results show that the improved model achieves a 5% increase in mAP and mAP50-95 values compared to the original YOLOv5s model in detection, and an accuracy improvement of over 2.2% in classification compared to commonly used algorithms. The lemon quality grading detection method based on improved YOLOv5s achieves improved performance in both detection and grading tasks, meeting the practical application needs of the market.