# Robust Miner Detection in Challenging Underground Environments: An Improved YOLOv11 Approach
## 2 Materials and Methods
### Mine Worker Detection Network Structure  
As YOLOv11 achieves structural advancements in the object detection domain, providing both real-time detection efficiency and precision [19], this paper selects it as the baseline network. Its architecture comprises three main modules: the backbone network, the neck network, and the head network, each incorporating innovative design elements to enhance performance. The backbone network adopts a transformer-based structure, effectively capturing long-range dependencies and global contextual information in images, which aids in detecting small targets and partially occluded objects. YOLOv11 introduces the C3k2 (Cross Stage Partial with a kernel size of 2) module, allowing feature partitioning through more efficient convolutional computations, thereby reducing computational load while improving feature representation capability. The neck network enhances detection performance further through multi-scale feature aggregation, utilizing the Spatial Pyramid Pooling Fast (SPPF) module and the Cross Stage Partial with Spatial Attention (C2 PSA) module to integrate selective spatial attention mechanisms across different feature scales, enhancing accuracy in feature extraction and key information focus. The head network incorporates a dynamically adaptive detection head, which automatically adjusts computational resource allocation to improve detection efficiency in complex scenes. Moreover, the  
Figure 1: Representative images from the dataset.  
model introduces a Non-Maximum Suppression (NMS) replacement algorithm and dual label assignment mechanism, increasing accuracy in overlapping and dense target scenarios while reducing inference latency. Overall, the YOLOv11 model achieves higher frame rates and average precision, with efficient feature extraction and detection capabilities suitable for high-performance computing environments and adaptable for edge device deployment, broadening its potential in real-time applications [20].  
This paper further proposes an improved network structure called the YOLOv11_ECA network, based on the YOLOv11 model, to further improve the detection precision in complex real-world scenes resulting from occlusion, low illumination, and partial interference caused by strong light. In this respect, it involves the mechanism of ECA above the SPPF layer to strengthen the learning ability in channel attention relations of great importance for maintaining the accuracy of occlusion condition detection. In addition, we propose a new loss function better suited to complex environmental conditions for single-class object detection in mining. This comprises weighted CIoU and an adaptive confidence loss component for better optimization of localization precision and robustness. This loss function mainly pays attention to the emphasis of certain predictions by dynamically adjusting its penalty scale according to the confidence level, thus allowing better results in unfavorable conditions. The use of a customized loss function is foreseen to increase model accuracy and robustness in real-world mining scenarios where object visibility could be compromised by varied lighting and physical obstructions. The improved network structure is shown in Figure 2. The detailed structures and algorithms of each module will be described in the following subsections.