# Robust Miner Detection in Challenging Underground Environments: An Improved YOLOv11 Approach
## 1 Introduction
(2020) presented some optimization of deep learning models to increase the accuracy of object detection in occlusion and dust interference complex mining environments. [PERSON] et al. (2024) [PERSON] et al. (2024) designed a real-time personnel detection system for underground coal mine environments with deep learning-based systems. This paper greatly improves the efficiency and accuracy of underground mine safety personnel detection. [PERSON] et al. (2024) [PERSON] et al. (2024) also developed a system that integrated infrared and thermal imaging to monitor coal mine safety and detect personnel. For example, under bad visibility conditions in the coal mine environment, it can accurately measure the human body. In a nutshell, all these works constitute an important advance in the application of deep learning to coal mine safety. Therefore, the improvement will be in real-time detection, enhancement of the model under difficult conditions, and integration of technologies like RFID for boosting the safety of personnel and efficiency in monitoring. YOLOv11, the latest version of the YOLO series, has been continually enhanced to improve detection accuracy and speed through progressive improvements to the model structure [PERSON] et al. (2024). However, due to the complex environment in coal mines, relying solely on the original YOLOv11 might be insufficient for addressing the challenges of underground mining scenarios, such as illumination variations, strong light interference, dust occlusion, and diverse worker attire. In this paper, an improved version of YOLOv11 that integrates the Efficient Channel Attention (ECA) mechanism is proposed to enhance the model's feature extraction capability. The ECA mechanism is used to effectively capture inter-channel dependencies, thereby improving feature representation, particularly in highlighting target features and suppressing irrelevant information in complex backgrounds [PERSON] et al. (2024). Furthermore, for the specific task of miner detection in underground coal mines, a weighted Complete Intersection over Union (CIoU) and an adaptive confidence loss function are introduced to improve localization accuracy and enhance model robustness. The adaptive weighting design helps the model focus on uncertain target regions, especially in low-light and occluded conditions. The main contributions of this paper include the following:  
1. A dataset for miner detection in underground coal mines has been innovatively constructed, encompassing complex environments such as low light, partial strong light interference, and occlusion, which effectively supports the model's detection performance in real mining scenarios. 2. The ECA attention mechanism has been incorporated into YOLOv11, enabling the model to focus more effectively on key features, thereby improving detection accuracy in complex environments. 3. A weighted CIoU and adaptive confidence loss function have been proposed, where the adaptive weighting design enhances the model's attention to uncertain target areas, particularly under low-light and occlusion conditions. 4. A detailed analysis of the detection efficiency and accuracy of the improved model has been conducted, comparing it with various other attention mechanism enhancements, different backbone improvements of YOLOv11, and mainstream object detection models. Additionally, the performance of different algorithms under extreme conditions, such as reduced brightness and noise interference, has been tested. Experimental results show that the proposed method achieves significant performance improvement and strong robustness in miner detection tasks. This study not only advances the technological development of intelligent safety detection in coal mines but also provides an important reference for the future intelligent management of mining operations, aiming to offer a higher level of safety assurance for coal mine workers.