Automatic crimping state classification in X-ray images of crimped tension clamp

The crimping states of the tension clamp can typically be recognised by using X-ray radiography. However, it is challenging to process X-ray images directly for inspection, especially when the crimping state and position of the groove are unclear. To address this problem, this paper firstly adopted the Beta greyscale stretching algorithm to enhance the X-ray images by increasing the contrast and reducing the noise. Subsequently, the Canny algorithm was implemented to locate and segment the groove region. Finally, the Grey-Level Co-occurrence Matrix (GLCM) method was applied to extract the texture features of the groove region, and identify the groove crimping state using support vector machine (SVM) classifier. Three types of the crimping states were utilised as test objects for classification. The results of the experiments demonstrated that the Beta enhancement algorithm, GLCM features used in this paper can effectively improve the accuracy rate of SVM classifier up to 99.51%. This study applies image processing technology towards the crimping states classification of tension clamp with accurate detection results enabling early-stage detection of abnormal crimping states leading to timely repairs.