To address the shortcomings of manual detection of thermal barrier coating topographic features, such as complexity and large errors, a method for automatically identifying topographic features of thermal barrier coatings using machine vision and calculating topographic feature parameters is proposed in this study. First, splat contours are automatically extracted based on a mathematical morphology and calculation of spread morphological parameters. The maximum interclass variance method is next used to obtain the binary segmentation threshold and the median filter and morphological operations are used to denoise the image and ensure a single splat. The connectivity of the splat is then obtained by contour extraction, and the solidity parameter of the splat is finally calculated according to the extracted contour. Simultaneously, this study realizes automatic identification and length calculation of cracks in thermal barrier coatings based on a traversal search. First, the lamellae in the image are identified and removed, and the fractured crack is repaired by the closing operation. The cracked skeleton is next obtained through image refinement, and each crack is then traversed and searched to complete the length calculation. The results show that the method effectively detects the splat profile and identifies cracks, has a good anti-noise interference ability, and can accurately calculate topographic feature parameters. Thus, this method can play a critical role in promoting the study of the deposition behavior of thermal spray droplets on the surfaces of substrates.