# A Lithology Recognition Network Based on Attention and Feature Brownian Distance Covariance
###### Abstract  
In the context of mountain tunnel mining through the drilling and blasting method, the recognition of lithology from palm face images is crucial for the comprehensive analysis of geological conditions and the prevention of geological risks. However, the complexity of the background in the acquired palm face images, coupled with an insufficient data sample size, poses challenges. While the incorporation of deep learning technology has enhanced lithology recognition accuracy, issues persist, including inadequate feature extraction and suboptimal recognition accuracy. To address these challenges, this paper proposes a lithology recognition network integrating attention mechanisms and a feature Brownian distance covariance approach. Drawing inspiration from the Brownian distance covariance concept, a feature Brownian distance covariance module is devised to enhance the network's attention to rock sample features and improve classification accuracy. Furthermore, an enhanced lightweight Convolutional Block Attention Module is introduced, with upgrades to the multilayer perceptron in the channel attention module. These improvements emphasize attention to lithological features while mitigating interference from background information. The proposed method is evaluated on a palm face image dataset collected in the field. The proposed method was evaluated on a dataset comprising field-collected images of a tunnel rock face. The results illustrate a significant enhancement in the improved model's ability to recognize rock images, as evidenced by improvements across all objective evaluation metrics. The achieved accuracy rate of 97.60% surpasses that of the current mainstream lithology recognition neural network. Keywords:lithology recognition; deep residual network; feature Brownian distance covariance; attention mechanism; deep learning +
Footnote †: journal: Medical  
technological intricacies and challenging labor environments, is compounded when faced with adverse geological and topographical features, such as karst formations, high-stress zones, fault fracture areas, and rock bursts. The excavation of highway tunnels, typically characterized by expansive spans and flattened shapes, necessitates stringent drainage requirements throughout the digging process. Presently, the predominant method for highway tunnel excavation involves drilling and blasting. While effective, this method can disrupt the surrounding rock significantly, increasing the risk of tunnel collapse incidents. The associated challenges in tunnel construction, particularly when dealing with adverse geological features, pose elevated demands on the design, construction, and management of highway tunnels. Despite advancements in tunnel excavation methods, some professional skills pertinent to highway tunnel construction remain underutilized. The prevalent use of the New Austrian Method, encompassing surface blasting technology, spray anchor support technology, and perimeter rock deformation measurement technology, underscores the need for practical expertise and comprehensive research within tunnel construction teams. The absence of a practical mastery of these techniques, coupled with a lack of understanding of the underlying principles, can result in unnecessary waste during tunnel excavation, ultimately inflating project costs and diminishing economic benefits [6]. Deep learning-based recognition methods have demonstrated the potential to enhance result objectivity and alleviate the workload of analyzers compared to traditional methods [7]. Over the years, the continuous evolution of deep learning technology has notably elevated the accuracy of rock properties recognition [8; 9]. [PERSON] et al. [10] successfully employed a convolutional neural network algorithm to classify three rock properties--granite, kilomagnet, and breccia--with a recognition accuracy of 90%. [PERSON] et al. [11] utilized a self-learning intelligent algorithm for the automatic classification of nine rock sample images. [PERSON] et al.