篇名 |
Application of Improved Convolutional Neural Network in Defect Identification of Exhaust Pipe Welds
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並列篇名 | Application of Improved Convolutional Neural Network in Defect Identification of Exhaust Pipe Welds |
作者 | Qingfang Liu、Xiaoning Bo、Jinrong Xu、Jin Wang、Honglan Li |
英文摘要 | This article focuses on the identification of welding defects in engine exhaust pipe welds. Firstly, a binocular vision system is built, and the models and parameters of the cameras and lenses involved in the entire system are explained in detail. At the same time, the cameras are calibrated; Then, in response to the problems of large volume, low efficiency, and lack of attention mechanism in the current neural network model, the network model was improved by adding MP structure, CA attention mechanism, and other methods to improve the recognition efficiency of the model. Finally, the reliability of the proposed method was verified through simulation experiments, and the overall recognition efficiency was improved to 97.28%.
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起訖頁 | 135-150 |
關鍵詞 | deep learning、welding seam、binocular vision、YOLOv7 |
刊名 | 電腦學刊 |
期數 | 202404 (35:2期) |
DOI |
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QR Code | |
該期刊 上一篇
| Design of Automotive Active Suspension System and Simulation for Intelligent Control Strategy |
該期刊 下一篇
| Research on Computer Aided Deformation Control Strategy for Welding of Large Long Straight Beams |