Image sharpening method of automatic loading and unloading drill pipe target in underground coal mine

Due to the space limitation of underground roadway in coal mine, the drilling robot is designed with two independent systems, i.e., the drilling system and the drilling pipe loading and unloading system, which are in flexible layout. Using the monocular visual identification based on cooperative target, the drilling system is associated with the drilling pipe loading and unloading system for the cooperative operation of the two systems to deliver the drill pipe. However, there are problems such as borehole cinder, cooperative target polluted by sewage, high dust concentration, low visibility, low luminance of artificial light source, uneven illumination resulting in low contrast of target image, and the reflection of cooperative target under the direct lighting of mining lamp in the complex scenarios of drilling construction in the underground coal mine, which will affect the resolution of cooperative target image and reduce the accuracy of target identification and the position to load and unload the drill pipe, thus influencing the success rate of automatic loading and unloading of drill pipe by drilling robot. Therefore, in view of the problem of cooperative target pollution by coal cinder and coal slime returned from the borehole in construction, the open operation of first corrosion and then expansion in morphological filtering was used to remove the discontinuous patches and prominent target pixels of coal cinder. Meanwhile, the closing operation of first expansion and then corrosion was also adopted to remove the small holes and intermittent small gaps, thereby eliminating the impact of solid pollutants on the cooperative targets. Besides, to eliminate the effect of dust, rock dust, water mist and sewage on image contrast, the initial transmittance was obtained by dark channel prior, the transmittance was refined by gradient domain guided filtering, the dark channel was improved by the guided filtering, and the atmospheric light value was solved. On this basis, the defogged image was obtained with the atmospheric scattering model, so as to realize the purpose of sharpening the image and enhancing the image contrast. Finally, sub-pixel corners and directions were refined, and the energy function was optimized. In addition, the seed checkerboard was used to grow checkerboard along the four edges, and the energy changes were used to confirm whether the largest checkerboard was grown, and thus the missing cooperative target image due to strong light reflection was completed. The experimental results show that the combination of morphological filtering, defogging algorithm and growth based checkerboard corner detection method could enhance the resolution of the cooperative target image in the complex scenarios of drilling construction in underground coal mine and improve the image accuracy based on visual identification, laying a foundation for the precise control of automatic loading and unloading of drill pipe by the drilling robot.