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Cable replacement has been commonly implemented to avoid its insufficient bearing capacity after long-term service. To guide better the cable maintenance and life assessment, the experimental studies on replaced cables are significant. As the fundamental step of replaced cable assessment, the corrosion examination of steel wires is required. The existing methods and systems mainly focus on the qualitative estimation and localization of corrosion, which is hard to fulfill the requirement of precise analyses. In this paper, a multi-vision scanning system is proposed to detect and represent the real corrosion and its distribution on the surface of a steel wire. Applied with a novel high-definition image scanning hardware and advanced panoramic image stitching processing algorithms, the surface 2D texture of a single steel wire can be scanned automatically. Afterward, by using the deep learning methods, the corrosion is precisely identified. Furthermore, non-uniformity indicators are proposed for the description of the apparent corrosion. The system has been verified and utilized in the inspection and evaluation of several steel wires from a replaced suspender. Due to it is effective and economical, the system is promising and serves as an instrument for both corrosion researches and structural assessment. [Display omitted] • Design and develop a novel flexibly extensible multi-vision system for visual examination of steel wires. • Deduct and accomplish the image processing algorithms for standard panoramic image acquisition of steel wires. • Propose the evaluation indicators reflecting the non-uniformity of corrosion by image identification of the panoramic image of steel wires. [ABSTRACT FROM AUTHOR]