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| Machine Vision-based Research on the Inspection of Dropper Defects of Overhead Contact Line |
| LIU Jie1, XU Jianguo1, GAO Chunli2, LIU Qiuhang3 |
1. China Railway Electrification Bureau Group Co. Ltd, Beijing 100036, China;
2. Dalian Weide Integrated Circuit Co. Ltd, Dalian, Liaoning 116085, China;
3. Beijing Institute of Technology, Beijing 100081, China |
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Abstract Research purposes: To solve the problem of low efficiency and long time due to manual inspection, we create an intelligent inspection system of dropper defects of overhead contact line by applying high-definition image acquisition technology and image defect recognition modelling technology.
Research conclusions: (1) By introducing visual intelligence technology, the research can establish the image detection standard for the installation status of overhead contact line dropper, create a database of defect images of overhead contact line dropper, and perform dynamic intelligent and rapid recognition and detection of overhead contact line dropper defect status. (2) The system uses the Faster R-CNN neural network model to train the defect data of overhead contact line dropper. With data transformation processing on the image data, the mean average precision (mAP) of the training results can reach 81.36%. (3) The system has high recognition rate accuracy, whichcan improve the detection efficiency and reduce omission. (4) The research results can provide a new way to change the traditional overhead contact line inspection method into intelligent inspection, and have certain reference value.
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Received: 27 September 2021
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2022, Vol. 39 
