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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(yè)有限公司，浙江 杭州 314100）

摘要：輪胎是我國國民經(jīng)濟的重要支柱，利用X光機對輪胎進行質(zhì)量檢測在整個輪胎生產(chǎn)過程中是極其重要的一道工序。目前國內(nèi)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(fā)展方向。本文將目標檢測算法Faster R-CNN應用于輪胎質(zhì)檢，并加以改進：（1）在模型中融合FPN（Feature Pyramid Network，特征金字塔網(wǎng)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