Production technology has increased rapidly with the development of industrial technology. Conventional human visual inspection is insufficient for conducting quality control under this increased capacity. Therefore, high-speed and high-accuracy automated optical inspection is becoming increasingly crucial. In this article, we propose an automated inspection method for a compact camera lens using a circle Hough transformation, weighted Sobel filter, and polar transformation. Our analysis of defects in the compact camera lens identified problems including of the circular texture and the non-fixed position of the inspection region. To overcome these problems, we design an inspection algorithm for locating and inspecting a circular region. A machine learning support vector machine method is then applied for obtaining a precise detection result. The experimental results show that the proposed inspection method is suitable for detecting defects in a complicated circular inspection region, and that the proposed system exhibited high performance.
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