Chipping defects on wafer dicing lanes severely impair wafer dicing quality, creating an urgent need for high-precision detection technology to support quality assessment. This paper proposes a sub-pixel edge detection method based on Gaussian fitting. First, image quality is optimized, and dicing lane features are isolated through region of interest (ROI) extraction. Second, accurate initial localization of pixel-level edges is achieved. Finally, edge points are selected with the pixel-level edge point as the center; a sub-pixel edge detection algorithm based on Gaussian peak position estimation is used to calculate sub-pixel coordinates via Gaussian integral curve fitting. Combined with random sample consensus（RANSAC） line fitting and density-based spatialclustering of applications with noise（DBSCAN） clustering, the identification and quantification of abnormal regions are completed. Experiments were conducted using white light interferometer measurements as the reference. Tests on 20 dicing lane samples show that the error range is -1.92~3.73 μm. Compared with the Zernike moment sub-pixel algorithm, interpolation method, and traditional Gaussian fitting algorithm, this method exhibits lower error and better stability, and can provide a reliable technical solution for industrial batch detection.