Downhole Track Detection via Multi-dimensional Conditional Generative Adversarial Nets

Abstract

Frequent mine disasters cause a large number of casualties and property losses. Autonomous driving is a fundamental measure for solving this problem, and track detection is one of the key technologies for computer vision to achieve downhole automatic driving. The track detection result based on the existing models lacks the semantic detail of the tracks and relies too much on visual postprocessing technology. Therefore, this paper proposes a track detection model based on the multi-dimensional conditional generative adversarial network. First, the generator is decomposed into global and local parts using a multi-granularity structure. Second, a multi-scale shared convolution structure is introduced into the discriminator to further guide the generator. In addition, this paper proposes a penalty mechanism based on Monte Carlo search to enhance the semantic constraints in the image generation process. Compared with the state-of-the-art semantic segmentation algorithms, extensive experiments on the downhole scene dataset demonstrate proposed model achieved the best results in terms of pixel accuracy, intersection-over-union (IOU) and the track detection accuracy. This paper provides a new idea for track line detection. In the future, the model can also be applied to other segmentation problems as well. Code and data will be shared.