隨著電腦視覺技術的發展，越來越多的公司正在用電腦視覺取代人工金屬表面缺陷檢測。然而，傳統的影像處理方法在處理影像時往往面臨巨大的挑戰，包括消除雜訊、消除失真和偵測微小的物體，導致計算效率和檢測精度低下。因此，本文設計了一個完整的缺陷檢測系統，旨在實現客製化輪胎模具上每個缺陷的精確定位。我們設計了一個檢測平台，並提出了一種新穎的斑點檢測方法，該方法靈敏度高，無需使用過濾器即可消除噪音。我們通過U2-Net去除影像中不必要的部分，以避免檢測到模具外的缺陷特徵。我們建立了一個輪胎模具數據集-STM來訓練和評估我們的方法。最後，我們利用表面缺陷檢測方法與檢測平台相結合，實現了83.6%的缺陷檢測任務的精度，以及87.9%的召回率，從而實現了生產線的品質檢測，並且檢測時間從原本10幾分鐘縮減到30秒，大幅降低模具瑕疵檢測的時間。

With the advancement of computer vision technology, an increasing number of companies are replacing manual metal surface defect detection with computer vision. However, classical image processing methods often face significant challenges when dealing with images, including noise, distortions, and tiny objects resulting in low computing efficiency and detection accuracy. Therefore, this paper designs a complete defect detection system that aims to achieve the precise location of each defect on the customized tire mold. We designed an inspection platform and proposed a novel blob detection method that is highly sensitive and can eliminate noise without using a filter. In addition, we removed unnecessary parts of the image through U2-Net to avoid detecting defective features outside the mold. Moreover, we set up a tire mold dataset—STM for training and evaluating our method. Finally, we achieved 83.6% precision and 87.9% recall rate for the defect detection task utilizing a surface defect detection method combined with an inspection platform that enables production line quality inspection. In addition, the inspection time was decreased from around 10 minutes to 30 seconds, a considerable reduction in the time necessary for mold defect inspection.

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