X‐ray‐based machine vision technique for detection of internal defects of sterculia seeds. Issue 8 (5th July 2022) - Xue, Qilong; Miao, Peiqi; Miao, Kunhong; Yu, Yang; Li, Zheng
Abstract : Abstract: An online machine learning system based on X‐ray nondestructive quality evaluation technique was developed to detect internal defects of boat‐fruited sterculia seed. The X‐ray images of boat‐fruited sterculia seed were first acquired by the detection system. Then, a boat‐fruited sterculia seed net (BSSNet) was trained to identify the defective boat‐fruited sterculia seeds based on the X‐ray images. The BSSNet was evaluated with the accuracy, precision, specificity, and sensitivity as 94.64%, 93.51%, 92.37%, and 96.64%, respectively. Further, three classical CNN models including VGG16, Resnet, and Inception were trained on the same dataset with accuracy of 95.71%, 94.29%, and 94.64%, respectively. Compared with the classical CNN models, the BSSNet achieved similar or higher accuracy in X‐ray images classification. Finally, an independent dataset containing 200 X‐ray images was used to validate the performance of the BSSNet and obtained an accuracy of 96.5%. The results presented above demonstrated that this classification method has a great potential for industrial applications. Practical Application: An X‐ray online detection system integrated with a machine vision model was used to evaluate the quality of boat‐fruited sterculia seed. A low‐power x‐ray detection system can detect internal defects of the object and ensure safety in the production process. The developed machine vision can sort the boat‐fruited sterculia seed with an accuracy of 96.5%. The proposed nondestructive detection system showed a good potential to be used in industrial applications.