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In order to improve the obstacle avoidance ability of inspection robots in complex environments and enable them to safely complete inspection tasks, an automatic control system for obstacle avoidance trajectory of inspection robots based on deep learning is designed. Design an industrial intelligent visual CCD camera composed of CCD sensors, signal processing chips, and other devices. Based on FPGA and USB2.0 video capture cards, transmit and collect data, and complete the hardware design. In software design, preprocessing such as target segmentation and binocular target matching are implemented on the collected images. The three-dimensional information of obstacle spatial position is obtained through binocular visual calibration of the camera. Based on CRNN in deep learning, a robot autonomous obstacle avoidance planning network model is designed, and a fuzzy trajectory controller is designed to achieve automatic trajectory control in obstacle avoidance. The system testing results show that the designed system successfully avoided three dynamic obstacles, with a maximum trajectory control error of 1.45° and a maximum minimum trajectory control error of 0.62°. The dynamic obstacle avoidance inspection speed is always around 3.5m/s, demonstrating precise and stable trajectory control effects.