The cable patrol patrol robot can distinguish the fire door by image recognition technology and realize the function of crossing the fire door autonomously. However, the effect of image recognition is greatly affected by light intensity. In some cable tunnels, the dim environment makes the effect of image recognition fire door poor, which seriously affects the passage of robots. In order to improve the identification accuracy of fire door in dim environment, a method of tunnel fire door identification by patrol robot in dim environment was proposed. Firstly, the original image was preprocessed, and the image enhancement technology was mainly used to enhance the details of the collected image through guided filtering. The saliency detection algorithm based on feature clustering and multi-scale fusion can effectively reduce the interference of background information and false target, and obtain the feature image of target image. A target recognition network based on improved YOLO-V4 was adopted, which added one auxiliary network, which can effectively improve the performance of the whole feature extraction network. The attention mechanism was adopted to fuse the feature information of the auxiliary network and the backbone network, which enhanced the effective information channel, suppressed the invalid information channel, and improved the efficiency of network recognition. The experimental results show that this method can improve the accuracy of image recognition in dim environment and effectively solve the problem that patrol robot cannot accurately identify the fire door due to insufficient light.
Combined with the principle of underwater signal transmission, this paper introduces the advantages and disadvantages of various docking and positioning technologies for submarine cable inspection robot to realize underwater charging. On this basis, a multi-sensor fusion docking and positioning technology is proposed, which is applied to the charging and docking guidance of underwater robot, and improves the accuracy and success rate of docking operation.
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