Segmentation of small defects in Final Optics Damage Online Inspection images

Automotive detection of defects plays an important role in Final Optics Damage Online Inspection (FODOI). Because of the tiny size of defects compared to the image, detection of the defects is a challenge. In a 2k×2k FODOI image, the size of the defects is only several pixels. Moreover, the gray value of different image areas is different because of the uneven distribution of illumination. In this paper, a robust defects detecting method based on Local Area Signal Strength (LASS) and 2-D histogram is theoretically and experimentally proposed. After the image stretching process achieved by grayscale morphology arithmetic, we computed the LASS at every pixel in the image, resulting in a new image with LASS values for each pixel. Thus, an adaptive threshold selection algorithm in 2-D histogram formed by LASS was achieved. The proposed algorithm was compared with some other fast algorithms, such as the traditional 2-D histogram algorithm, Otsu algorithm, and the FCM clustering algorithm; the results show that the proposed algorithm has higher detection precision and is robust in uneven illumination.