Han Cai, Liu Zhenwen, Zhao Shenglei, Li Yubo, Duan Yanwei, Yang Xinzhou, Hao Chuanbo
School of Safety Engineering, Heilongjiang University of Science and Technology, Harbin, 150022, China.
Science and Technology Department of Heilongjiang, University of Science and Technology, Harbin, 150022, China.
Sci Rep. 2025 Apr 29;15(1):15023. doi: 10.1038/s41598-025-00016-3.
The existing coal-rock identification technology based on machine vision makes it difficult to accurately identify coal-rock images with low distinguishability. To solve this problem, a special coal-rock environment simulation experimental device was used to conduct simulations, considering various influencing factors such as illumination, air flow, coal dust, and water mist concentration. We characterized the grayscale and texture feature patterns of coal-rock media under varying degrees of interference and established a comprehensive multi-element image training sample library. The simulation experiment results show that illumination, dust, and fog can reduce the distinguishability of coal-rock images, which seriously affects the recognition performance of the network. Based on this, the convolution operation was combined with the Vision Transformer network and the deep convolution algorithm was applied to design a parallel hybrid vision network model, PFVnet. Subsequently, enhanced recognition tests were carried out in combination with the DeepLabV3 + network. The test results show that PFVnet can enhance the features of coal and rock, and achieve a PSNR of 18.90 and an SSIM of 0.58 on the multi-element image training sample library. It can effectively reduce the misjudgment of the DeepLabV3 + network, increasing its accuracy by 0.95%, the mean Intersection over Union (mIoU) by 2.15%, and the mean Pixel Accuracy (mPA) by 2.12%. This research provides new ideas and feasible technical solutions for the improvement of coal-rock identification technology and helps to promote the development of this field.
现有的基于机器视觉的煤岩识别技术难以准确识别可区分性低的煤岩图像。为解决这一问题，使用一种特殊的煤岩环境模拟实验装置进行模拟，考虑了光照、气流、煤尘和水雾浓度等各种影响因素。我们表征了不同干扰程度下煤岩介质的灰度和纹理特征模式，并建立了一个综合多元素图像训练样本库。模拟实验结果表明，光照、灰尘和雾气会降低煤岩图像的可区分性，严重影响网络的识别性能。基于此，将卷积运算与视觉Transformer网络相结合，并应用深度卷积算法设计了一种并行混合视觉网络模型PFVnet。随后，结合DeepLabV3 +网络进行了增强识别测试。测试结果表明，PFVnet可以增强煤岩特征，在多元素图像训练样本库上实现了18.90的峰值信噪比（PSNR）和0.58的结构相似性指数（SSIM）。它可以有效减少DeepLabV3 +网络的误判，将其准确率提高0.95%，平均交并比（mIoU）提高2.15%，平均像素准确率（mPA）提高2.12%。本研究为煤岩识别技术的改进提供了新思路和可行的技术方案，有助于推动该领域的发展。
