Nondestructive Detection and Quality Grading System of Walnut Using X-Ray Imaging and Lightweight WKNet

文献类型： 外文期刊

第一作者： Fan, Xiangpeng

作者： Fan, Xiangpeng;Fan, Xiangpeng;Zhou, Jianping;Zhou, Jianping

作者机构：

关键词： walnut internal quality; nondestructive detection; X-ray imaging; YOLO v5s; lightweight model

期刊名称：FOODS （ 影响因子：5.1； 五年影响因子：5.6 ）

ISSN：

年卷期： 2025 年 14 卷 13 期

页码：

收录情况： SCI

摘要： The internal quality detection is extremely important. To solve the challenges of walnut quality detection, we presented the first comprehensive investigation of walnut quality detection method using X-ray imaging and deep learning model. An X-ray machine vision system was designed, and a walnut kernel detection (called WKD) dataset was constructed. Then, an effective walnut kernel detection network (called WKNet) was developed by employing Transformer, GhostNet, and criss-cross attention (called CCA) module to the YOLO v5s model, aiming to solve the time consuming and parameter redundancy issues. The WKNet achieved an mAP_0.5 of 0.9869, precision of 0.9779, and recall of 0.9875 for walnut kernel detection. The inference time per image is only 11.9 ms. Extensive comparison experiments with the state-of-the-art (SOTA) deep learning models demonstrated the advanced nature of WKNet. The online test of walnut internal quality detection also shows satisfactory performance. The innovative combination of X-ray imaging and WKNet provide significant implications for walnut quality control.

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