基于深度学习的耐张线夹压接缺陷X射线影像智能识别

X-Ray Image Intelligent Recognition of Crimping Defects of Strain Clamps Based on Deep Learning

输电线路中耐张线夹压接质量影响着电网运行安全,目前对耐张线夹压接质量检测方法主要是拍摄X射线图像并进行人工识别。但由于耐张线夹X射线图像存在缺陷部位尺寸小且排列紧密等特点,人工方法显得耗时费力且准确率不高。针对上述问题,提出一种基于深度学习的耐张线夹压接缺陷X射线图像检测系统。采用分级检测原则,首先利用CenterNet算法定位存在缺陷的压接部位并切割出压接部位,增大压接缺陷在图像中的占比,其次利用数据增强扩充数据集,最后利用RetinaNet算法检测压接缺陷。经验证,该分级检测策略与采用传统检测算法相比,在准确率和检测速度上都有一定程度提升,可满足实际工程中应用要求。

The crimping quality of the strain clamp in the transmission line affects the safety of the power grid.At present,the quality inspection method of the strain clamp crimping is mainly to take X-ray images and perform manual identification.However,due to the small size and tightly packed of the defect parts in the X-ray image of the strain clamp,the manual method appears to be time-consuming and labor-intensive and the accuracy rate is not high.Aiming at the above problems,an X-ray image detection system for crimping defects of strain clamps based on deep learning is proposed.The principle of hierarchical detection is adopted,Firstly,the CenterNet algorithm is used to locate the defective crimping part and cut out the crimping part to increase the proportion of crimping defects in the image.Secondly,the data are used to enhance the data set,and finally the RetinaNet algorithm is used to detect the crimp defect.By verification that the hierarchical detection strategy in this paper improves to a certain extent in accuracy and detection speed compared with the traditional detection algorithm,which meets the application requirements in actual engineering.