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摘要
Objective: In recent years, the frequent occurrence of large-scale mine accidents has led to many casualties and property losses. The mines' production and transportation should be promoted in the way of intelligence. When moving under the mine, the controller of a locomotive needs track information to detect the presence of pedestrians or obstacles in front of it. Then, the locomotive slows down or stops as soon as an emergency condition appears. Track detection, which uses image processing technology to identify the track area in a video or image and displays the specific position of the track, is a key technology for computer vision to achieve automatic driving downhole. Track detection algorithms based on traditional image processing can be classified into two categories. The first is a feature-based approach, which uses the difference between the edge and surrounding environment to extract the track region and obtains the specific track location in the image. However, this method relies heavily on the underlying features of the image and is easily interfered by the surrounding environment, which brings great challenges to the subsequent work and affects the final detection effect of the track. The second is a model-based strategy, which converts the track detection into a problem of solving the track model parameters and achieves the fitting of the track based on its shape in the local range. The track model often cannot adapt to multiple scenarios, and the approach lacks robustness and flexibility. Track detection results based on the convolutional neural network algorithm lack a detailed, unique characterization of the object and rely heavily on visual post-processing techniques. Therefore, we propose conditional generative adversarial net, a track detection algorithm combining multiscale information. Method: First, the multigranularity structure is used to decompose it into global and local parts in the generator network. The global part is responsible for low-resolution image generation, and the local part will combine with the global part to generate high-resolution images. Second, the multiscale shared convolution structure is adopted in the discriminator network. The primary features of the real and synthesized samples are extracted by sharing the convolution layer, the corresponding feature map is obtained, and different samples are sent to the multiscale discriminator to supervise the training of the generator further. Finally, the Monte Carlo search technique is introduced to search the intermediate state of the generator, and the result is sent to the discriminator for comparison. Result: The proposed algorithm achieves an average pixel accuracy of 82.43% and a mean intersection over-union of 0.621 8. Moreover, the accuracy of detecting the track can reach 95.01%. For many different underground scenes, the track test results show good performance and superiority compared with the existing semantic segmentation algorithms. Conclusion: The proposed algorithm can be effectively applied to the complex underground environment and resolves the dilemma of algorithms existing in traditional image processing and convolutional neural network, thus effectively serving underground automatic driving. The algorithm has the following virtues. First, it generates high-resolution images by generative adversarial nets and addresses unstable training in generating edge features of high-resolution images. Second, the multitask learning mechanism is further conducive to discriminator identification, thereby effectively monitoring the results generated by the generator. Finally, the Monte Carlo search strategy is used to search the intermediate state of the generator, which is then feed it into the discriminator, thereby strengthening the constraints of the generator and enhancing the quality of the generated image. Experimental results show that our algorithm can achieve satisfactory results. In the future, we will focus on overcoming the issue of track line prediction under occlusion, expanding the datasets, and strengthening the speed, robustness, and practicality of the algorithm.
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