As an important equipment for intelligent operation and maintenance, inspection robots have been widely used in high-risk and complex scenarios such as power, mining, and chemical industries. The visual system, as the “eyes” of inspection robots, undertakes tasks including image enhancement, navigation and positioning, target recognition, and error correction, and its performance directly affects the robots’ autonomous operation capabilities. Currently, the visual algorithms of inspection robots still face several problems, such as poor adaptability to complex environments, insufficient navigation accuracy, difficulty in balancing target recognition accuracy and real-time performance, and weak adaptability of error correction. Combined with the current application status of inspection robots, this paper elaborates on the design ideas of the four major modules of visual algorithms and proposes optimization strategies for existing problems, providing references for improving the autonomous inspection capabilities of inspection robots and promoting the upgrading of intelligent inspection technology.