Abstract Gear contact spot plays a crucial role in evaluating gear mesh quality. Traditionally, tooth surfaces of gear pairs have been manually brushed with red lead powder and visually inspected after a certain operating period. To enhance detection accuracy and efficiency, computer vision has emerged as an appealing approach for gear contact spot detection. However, determining the graying weights and segmentation threshold poses challenges, particularly in non-ideal illumination environments. To address this problem, this paper proposes a new target color adaptive graying and segmentation (TC-AGS) method for the gear contact spot detection. With the proposed method, the illumination scene is firstly distinguished by the blue color component weight due to its high sensitivity to illumination intensity. Then, an improved adaptively graying algorithm for target color is employed to determine the graying weights for RGB, ensuring the maximum proportion of the desired red color. Finally, either a global threshold method or a local threshold method is selected for image segmentation and contact spot detection according to the illumination intensity. To validate the effectiveness of the proposed method, comprehensive simulation and experimental tests were conducted. The results demonstrate that the proposed method achieves better gear contact spot detection performance than traditional methods. It exhibits higher extraction accuracy under different illumination environments and even in the presence of blurred image sources.
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