To address the limited computing power of edge devices on pre-packaging vegetable sorting lines and the difficulty of balancing accuracy and model size in detecting small surface defects, the work aims to propose a lightweight detection model named GEF-YOLO (Ghost-ECA-Frequency Decoupling YOLO). With YOLOv8s as the baseline, the model was improved from three aspects of feature extraction, channel attention, and detection head. In the backbone and neck, GhostConv and C3Ghost replaced the standard convolutions and C2f modules, cutting channel redundancy through low-cost linear operations to shrink the model. An ECA module was added at the end of the backbone to strengthen the response to lesion features via cross-channel interaction without dimensionality reduction. A Frequency Decoupling Interaction Predictor (FDIP) was designed to replace the original decoupled head, decomposing features into low- and high-frequency components that were fed into the classification and regression branches respectively, so as to ease the feature-preference conflict between the two branches. The model was trained and tested on the public Fresh and Rotten Vegetable dataset (5 vegetable types, fresh and rotten states, 11 660 images). With precision, recall, mAP50, and mAP50-95 as metrics, ablation studies and comparisons with mainstream models were conducted, and detection results in complex scenes were analyzed qualitatively. Compared with YOLOv8s, GEF-YOLO reduced the Params and FLOPs by 54.4% and 47.5% respectively, with a precision of 92.83%, a recall of 90.47%, an mAP50 of 94.56%, and an mAP50-95 of 76.86%, with an inference speed of 116.73 frames/s, 11.1% higher than that of YOLOv8s. These results show that GEF-YOLO substantially compresses the model while maintaining accuracy comparable to the baseline, offering a practical lightweight solution for vegetable defect detection in resource-constrained settings.
Key words
vegetable defect detection /
YOLOv8s /
lightweight model /
frequency decoupling /
ECA attention mechanism
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