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Roboticized coal mining equipment is an important way to achieve intelligent coal mining. The perception system of robots is crucial for coal mine robots to understand their operating environment and conditions, serving as the functional foundation for autonomous operation in coal mines. However, due to the harsh and complexly changing conditions in underground coal mine environments, it is difficult for robotized coal mining equipment to obtain information about the underground environment accurately and reliably. In response to the characteristics of underground coal mine environments, an adaptive enhancement method for coal-mine underground images based on no-reference image quality evaluation is proposed in that paper. Its core is to establish a quality evaluation model for visible light images for underground coal mines and to develop an adaptive optimization algorithm for image enhancement parameters based on the improved sparrow search algorithm (SSA). Relevant experiments are conducted to demonstrate the performance of the proposed method. Experimental results show that, for three common enhancement methods, including contrast-limited adaptive histogram equalization (CLAHE), homomorphic filtering, and automatic multiscale etinex with color restoration (AutomateMSRCR) algorithms, the results obtained from the optimized algorithms are improved by an average of 41.28%, 2.67%, 19.06%, 43.62%, and 129.46%, respectively, in terms of the SMD function, entropy function, variance function, Brenner gradient function, and Laplacian gradient function indicators, and the proposed adaptive image enhancement method shows strong application potential for the scene perception tasks including the anchor-rod tail detection, etc.
Wei et al. (2024) studied this question.