This research focuses on the pivotal role of coal mining shaft safety. Deformations and fractures in the shaft wall can cause significant risks to mine operations, which require regular safety inspection to prevent hazards. Existing shaft detection systems cannot provide any prior notifications before damage occurs, with complex systems and low intelligence. To solve these problems, our proposed system has three key indicators: shaft deformation, shaft perpendicularity, and horizontal vibration of the lifting container. This intelligent system reduces the likelihood of significant shaft damage by enhancing automation, intelligence, and efficiency. The research starts by defining the essential features required for the shaft detection system, like data acquisition system, data transmission, and data processing. Hardware includes LiDAR sensors, CMOS cameras, laser collimators, control board, batteries, and software implemented on ROS. This system collects data, simulates shaft conditions, and conducts horizontal vibration displacement detection experiments. Moreover, we validated shaft deformation and perpendicularity detection under static conditions. This research concludes by analyzing the accuracy of the detection system in actual working conditions, confirming its practicality and reliability. Overall, this research solves critical issues in shaft safety and introduces an intelligent, efficient, and smart solution.