Metallic wire rope is the core component unit in construction, and vision-based nondestructive inspection methods for condition monitoring of metallic wire ropes have progressed in recent years. The lay length of metallic wire ropes contains the stress state and healthy state information of metallic wire ropes, which has important research significance and research value for the force balance guarantee of multirope structures and the safety of buildings. This article summarizes the shortcomings of the existing research of the lay length measurement methods and proposes a measurement method of the lay length of metallic wire ropes via deep learning and phase correlation image analysis algorithm. The segmentation of strand module applies the Mask-RCNN network to segment the strand into Voronoi-like images with high signal-to-noise ratio (SNR). Then, the phase correlation analysis is carried out on the obtained Voronoi diagram to calculate the lay length of the metal wire rope. The experimental results show that compared with the traditional manual method to measure the lay length of metal wire rope, the method proposed in this article greatly shortens the time required for lay length detection, only 0.1045 s. The average detection error of this method is 1.0672 mm, which is far lower than the traditional manual measurement method and the method of directly performing phase correlation image analysis on the metal wire rope. The reliability of the proposed method was validated by varying the lay length of metallic wire ropes using the tension-slack experimental device.