Non-destructive Internal Defect Detection of In-Shell Walnuts by X-ray Technology Based on Improved Faster R-CNN

The purpose of this study was to achieve non-destructive detection of the internal defects of in-shell walnuts using X-ray radiography technology based on improved Faster R-CNN network model. First, the FPN structure was added to the feature-extraction layer to extract richer image information. Then, ROI Align was used instead of ROI Pooling for eliminating the localization bias problem caused by the quantization operation. Finally, the Softer-NMS module was introduced to the final regression layer with the predicted bounding box for improving the localization accuracy of the candidate boxes. The results of the study indicated that the proposed network model can effectively identify internal defects of in-shell walnuts. Specifically, the discrimination accuracies of the in-shell sound, shriveled, and empty-shell walnuts were 96.14%, 91.72%, and 94.80%, respectively, and the highest overall accuracy was 94.22%. Compared to the original Faster R-CNN network model, the improved Faster R-CNN model achieved an increase of 5.86% in mAP and 5.65% in F-1-value. Consequently, the proposed method can be applied for the in-shell walnuts with shriveled and empty-shell defects.