Improved YOLOv11 Target Detection Model for Complex Coal Mine Environments

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Improved YOLOv11 Target Detection Model for Complex Coal Mine Environments

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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 48 Number of periods: 2027 XX Page number: 1-8 Column: Public date: 2027-12-10

Title:: Improved YOLOv11 Target Detection Model for Complex Coal Mine Environments

Author(s):: ZHANG Jianhui ^{1, 2} , CAI Xiaohang ¹ , WANG Ruimin ³ , ZENG Junjie ¹ , 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

Keywords:: YOLOv11; target detection; dynamic inception mixer convolution; multi-attention mechanism; efficient upsampling

CLC:: TP391 ；TD76

DOI:: 10.13705/j.issn.1671-6833.2026.04.009

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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Last Update: 2026-03-13