Visual detection of drilling robot position for rockburst prevention in mining processing by a new image dehazing method

Abstract

The positioning of drill pipe based on visual detection is a crucial aspect in achieving unmanned operation of drilling robot for rockburst prevention. However, the images directly collected from mechanized mining face are always polluted. In order to eliminate the interference of dust and haze in the image, an image dehazing method based on improved CycleGAN model is proposed in this paper. Firstly, a pipe image dehazing dataset for the rockburst prevention drilling robot is collected and established. Moreover, a generator architecture with a multi-scale U-shaped network structure is designed to improve the quality and accuracy of image recovery. A new reconstruction block is designed and an SK fusion layer is introduced to improve the feature extraction capability of the model, and a MU-CycleGAN network structure is constructed. Finally, an experimental platform for drill pipe image dehazing of the drilling robot for rockburst prevention is set up in the intelligent mining equipment laboratory. Experimental results showed that the PSNR and SSIM of the image dehazing model achieved 27.04 and 0.946, and the success rate of drill pipe grabbing has increased by 13.75%. Experimental results reveal that the proposed framework achieves superior image enhancement performance than the comparison algorithms.