Accurate segmentation of open-loop saw chain images under traction motion is the key to automatic detection of saw chain defects. In order to achieve accurate segmentation of parts in saw chain images, this paper proposes a saw chain image segmentation algorithm that combines assembly features and regression analysis. Firstly, by analyzing the assembly features of the saw chain, the Hough circle detection algorithm is used to initially obtain the position information of the rivets in the saw chain image; then the outlier elimination method based on the least squares method is established, and the missed rivets are judged by the position of the adjacent rivets, so as to solve the problem of false detection and missed detection in the Hough circle detection process; then perform affine transformation on the pixel coordinates of the adjacent rivet area to realize the segmentation of the blade, connecting piece and transmission piece in the saw chain image; finally, an experimental platform is built, and the algorithm is verified by collecting images with a dual-position camera. The experimental results show that the saw chain segmentation algorithm can accurately and quickly segment normal and defective saw chain images, and the saw chain segmentation accuracy rate reaches 94.4%, which has good reference significance and practical value for the automatic detection of similar products.