作者：

郭培林，陳金水，盧建剛（浙江大學，浙江 杭州 310058）

戴柏炯（杭州朝陽橡膠有限公司，浙江 杭州 310018）

史敦禹（中策橡膠（建德）有限公司，浙江 建德 311607）

孫洪林（杭州中策清泉實業有限公司，浙江 杭州 314100）

摘要：輪胎是我國國民經濟的重要支柱，利用X光機對輪胎進行質量檢測在整個輪胎生產過程中是極其重要的一道工序。目前國內工廠普遍采用肉眼觀察輪胎X光圖像進行識別，存在效率低下、人工成本高等一系列問題，因此采用計算機視覺技術進行自動識別是今后的發展方向。本文將目標檢測算法Faster R-CNN應用于輪胎質檢，并加以改進：（1）在模型中融合FPN（Feature Pyramid Network，特征金字塔網絡），用以解決輪胎瑕疵尺度變化大的問題；（2）在算法中融合背景特征信息，對候選框進行重排名，增加檢測模型最終的檢測精度。通過對某輪胎廠提供的輪胎X光圖像進行瑕疵檢測對比表明，這些改進措施提高了檢測模型的mAP（mean Average Precision）指標，具有良好的應用前景。

關鍵詞：輪胎X光圖像；瑕疵檢測；深度學習；背景特征

Abstract: The tire industry is an important part of China's nationale conomy. The use of X-ray machines for tie quality inspection is an extremely important process in the tire production process.At present, domestic factories generally use the naked eye to observe tire X-ray images for recognition. There are a series of problems such as low efficiency and high labor cost. Therefore,automatic identification by computer vision technology is the future development direction. In this paper, the Faster R-CNN is applied to tire quality inspection, and the following improvements are made: Firstly, FPN is integrated in the model to solve the problem of large changes in tire defect scale; secondly, the background feature information is integrated in the algorithm, the RoIs is re-ranked to increase the final detection accuracy of the detection model. The comparison of defect detection on the tire X-ray image shows that these improvement measures improve the mAP value of the detection model and have a good application prospect.

Key words: Tire X-ray image; Defect detection; Deep learning; Background feature

在線預覽：輪胎X光圖像瑕疵檢測Faster R-CNN算法改進研究

摘自《自動化博覽》2020年8月刊