Bolt connections play a crucial role in the manufacturing of crane steel structures; however, the potential issue of bolt loosening cannot be overlooked. Thus, regular inspection of bolt connections is essential. To automate the detection of bolt loosening, this paper proposes a batch detection method for bolt loosening angles based on machine vision. Firstly, YOLOv8 is employed for bolt target detection, and an improved lightweight segmentation network is used to achieve precise segmentation of the bolt areas. Subsequently, edge line detection and clustering of the segmented images are performed to accurately obtain all edge lines and corner points of the bolts. The centroids and reference points of the bolts are determined based on the corner points, completing the perspective correction. Finally, the corrected bolt corner points are compared with the reference corner points to measure the loosening angles of the bolts. Experimental results indicate that this method achieves high accuracy and sensitivity in bolt loosening measurements. Under different shooting angles, the maximum allowable loosening threshold is 2.62°, with a maximum relative error of only 6.6%, enabling accurate detection of minor bolt loosening. This method demonstrates excellent measurement accuracy and stability, showing promising engineering application prospects in the inspection of large steel structures.