<p>In micro milling machining, tool wear directly affects workpiece quality and accuracy, making effective tool wear monitoring a key factor in ensuring product integrity. The use of machine vision-based methods can provide an intuitive and efficient representation of tool wear conditions. However, micro milling tools have non-flat flanks, thin coatings can peel off, and spindle orientation is uncertain during downtime. These factors result in low pixel values, uneven illumination, and arbitrary tool position. To address this, we propose an image-based tool wear monitoring method. It combines multiple algorithms to restore lost pixels due to uneven illumination during segmentation and accurately extract wear areas. Experimental results demonstrate that the proposed algorithm exhibits high robustness to such images, effectively addressing the effects of illumination and spindle orientation. Additionally, the algorithm has low complexity, fast execution time, and significantly reduces the detection time in situ.</p>