[1]张建辉,等.改进 YOLOv11 的复杂煤矿井环境目标检测模型[J].郑州大学学报(工学版),2027,48(XX):1-8.[doi:10.13705/j.issn.1671-6833.2026.04.009]
　ZHANG Jianhui,CAI Xiaohang,et al.Improved YOLOv11 Target Detection Model for Complex Coal Mine Environments[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-8.[doi:10.13705/j.issn.1671-6833.2026.04.009]
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改进 YOLOv11 的复杂煤矿井环境目标检测模型()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
48
期数:
2027年XX
页码:
1-8
栏目:
出版日期:
2027-12-10

文章信息/Info

Title:
Improved YOLOv11 Target Detection Model for Complex Coal Mine Environments
作者:
张建辉1;  2 ;  蔡小航1 ;  王瑞民3 ;  曾俊杰1 ;  罗旭东1
1. 郑州大学 网络空间安全学院,河南 郑州 450002;2. 嵩山实验室,河南 郑州 450002;3. 郑州大学 计算机与人工智能学院,河南 郑州 450001
Author(s):
ZHANG Jianhui1;  2 ;  CAI Xiaohang1 ;  WANG Ruimin3 ;  ZENG Junjie1 ;  LUO Xudong1
1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, China; 2. Songshan Laboratory, Zhengzhou 450002, China; 3. School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
关键词:
YOLOv11;  目标检测;  动态感知混合卷积;  多注意力机制;  高效上采样
Keywords:
YOLOv11;  target detection;  dynamic inception mixer convolution;  multi-attention mechanism;  efficient upsampling
分类号:
TP391 ；TD76
DOI:
10.13705/j.issn.1671-6833.2026.04.009
文献标志码:
A
摘要:
针对煤矿施工环境中光照分布不均、目标严重遮挡、粉尘干扰等工况导致的目标检测精度不足的问题，提出一种基于DIM和YOLOv11的复杂煤矿环境目标检测模型DME-YOLO。在主干网络设计动态感知混合卷积模块（DIM），通过动态权重机制实现多尺度特征的自适应融合，提升复杂背景下的特征表征能力；在检测头部分引入动态多注意力机制检测头（DMA-Head），利用多尺度注意力模块增强对小目标和弱纹理目标的感知；在颈部网络中嵌入高效上采样卷积模块（EUCB），通过双线性插值与深度可分离卷积结合优化上采样路径。实验结果表明：DME-YOLO在自建矿井数据集上的mAP@50达到93.7%，较原始YOLOv11提升3.0个百分点；mAP@50-95达到66.8%，较原始YOLOv11提升5.2个百分点。与YOLOv9s、YOLOv12等模型对比，所提模型收敛速度更快且检测精度更优，适合煤矿井施工现场监测。
Abstract:
To address the insufficient target detection accuracy caused by harsh working conditions in coal mine construction environments, such as uneven illumination distribution, severe target occlusion, and dust interference, a target detection model named DME-YOLO was proposed for coal mine in complex environments based on DIM and YOLOv11. In the backbone network of DME-YOLO, a dynamic inception mixer convolution module (DIM) was designed. This module achieved adaptive fusion of multi-scale features through a dynamic weight mechanism, thereby enhancing the model’s capability of feature representation in complex backgrounds. For the detection head, a dynamic multi-attention detection head (DMA-Head) was introduced, which leveraged a multi-scale attention module to strengthen the perception of small targets and targets with weak textures. Additionally, an efficient upsampling convolutional block (EUCB) was embedded into the neck network optimizing the upsampling path by combining bilinear interpolation with depthwise separable convolution. Experimental results demonstrated that DME-YOLO achieved a mAP@50 of 93.7% on the self-constructed mine dataset, representing 3.0 percentage points improvement compared to the original YOLOv11. Its mAP@50-95 reached 66.8%, which was 5.2 percentage points increase relative to the original YOLOv11. When compared with models such as YOLOv9s and YOLOv12, DME-YOLO exhibited faster convergence speed and superior detection accuracy, making it well-suited for safety monitoring in coal mine construction sites.

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备注/Memo

备注/Memo:
收稿日期:2026-01-12;修订日期:2026-02-13
基金项目:国家重点研发计划(2023YFB2906400) ;河南省重大科技专项(221100210900)
作者简介:张建辉(1977— ) ,男,河南平顶山人,郑州大学副研究员,博士,主要从事网络信息安全与人工智能技术研究,E-mail:ndsczjh@ 163. com。
更新日期/Last Update: 2026-03-13