Zhang Lei, Sun Zhipeng, Tao Hongjing, Wang Meng, Yi Weixun
School of Coal Engineering, Shanxi Datong University, Datong 037000, China.
Sensors (Basel). 2024 Dec 31;25(1):170. doi: 10.3390/s25010170.
In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. This difficulty arises from factors such as uneven lighting conditions and equipment obstructions, which often lead to missed detections. Consequently, these limitations pose a considerable challenge to effective mine safety management. This article presents an enhanced algorithm based on YOLOv11n, referred to as GCB-YOLOv11. The proposed improvements are realized through three key aspects: Firstly, the traditional convolution is replaced with GSConv, which significantly enhances feature extraction capabilities while simultaneously reducing computational costs. Secondly, a novel C3K2_FE module was designed that integrates Faster_block and ECA attention mechanisms. This design aims to improve detection accuracy while also accelerating detection speed. Finally, the introduction of the Bi FPN mechanism in the Neck section optimizes the efficiency of multi-scale feature fusion and addresses issues related to feature loss and redundancy. The experimental results demonstrate that GCB-YOLOv11 exhibits strong performance on the dataset concerning mine personnel and safety helmets, achieving a mean average precision of 93.6%. Additionally, the frames per second reached 90.3 f·s, representing increases of 3.3% and 9.4%, respectively, compared to the baseline model. In addition, when compared to models such as YOLOv5s, YOLOv8s, YOLOv3 Tiny, Fast R-CNN, and RT-DETR, GCB-YOLOv11 demonstrates superior performance in both detection accuracy and model complexity. This highlights its advantages in mining environments and offers a viable technical solution for enhancing the safety of mine personnel.
在综采工作面的复杂环境中，当前的目标检测算法在实现对矿山人员和安全帽的最优精度和实时检测方面面临重大挑战。这种困难源于光照条件不均匀和设备遮挡等因素，这些因素常常导致漏检。因此，这些限制对有效的矿山安全管理构成了相当大的挑战。本文提出了一种基于YOLOv11n的改进算法，称为GCB - YOLOv11。所提出的改进通过三个关键方面实现：首先，用GSConv取代传统卷积，这显著增强了特征提取能力，同时降低了计算成本。其次，设计了一种新颖的C3K2_FE模块，该模块集成了Faster_block和ECA注意力机制。这种设计旨在提高检测精度，同时加快检测速度。最后，在Neck部分引入Bi FPN机制，优化了多尺度特征融合的效率，解决了特征损失和冗余相关问题。实验结果表明，GCB - YOLOv11在矿山人员和安全帽数据集上表现出强大的性能，平均精度达到93.6%。此外，每秒帧数达到90.3 f·s，与基线模型相比，分别提高了3.3%和9.4%。此外，与YOLOv5s、YOLOv8s、YOLOv3 Tiny、Fast R - CNN和RT - DETR等模型相比，GCB - YOLOv11在检测精度和模型复杂度方面都表现出卓越的性能。这突出了其在采矿环境中的优势，并为提高矿山人员安全提供了可行的技术解决方案。
