In the endless hot rolling process, camber is a significant defect where camber is flatness asymmetries produced by nonuniform distribution of rolling pressure. This kind of defect may not only cause clogging of the finishing mill, but also visible defects, such as bar edge folds, edge cracks, holes, and scrapes. It is important to measure the exact camber values of the overall shape and head/tail part of steel bars to reduce camber for hot-rolled steel bars. We introduce a vision-based camber measurement system based on two-area-scan charge-coupled device (CCD) cameras and proposes a camber-detection algorithm to achieve the optimal cutting-line, the overall bar shape, and camber values from the obtained image. The proposed algorithm consists of three parts: optimal cutting-line detection, image-stitching, and camber detection. The optimal cutting-line detection part determines the cutting line of the head/tail part of the bar for continuous joining between steel bars. The image-stitching part obtains the overall shape of a hot-rolled steel bar with continuous image sequence obtained by two-area-scan CCD cameras. The camber-detection part measures head camber, tail camber, and whole camber of the steel bar. Through the proposed camber-detection algorithm, the system not only satisfies a time constraint for detecting the camber of the steel bar but also obtains the optimal cutting line, the overall bar shape, and the camber values.