Bibliographic Details
| Title: |
面向综采工作面目标检测的 YOLO 算法研究现状及展望. |
| Alternate Title: |
Review of YOLO algorithm research for target detection in fully-mechanized mining face. |
| Authors: |
金智新1,2,3,4 jinzhixin@tyut.edu.cn, 史凌凯1,2, 耿毅德1,2,3,4 gengyide@tyut.edu.cn, 王宏伟1,2,3,4,5, 王浩然1,2,3,4,5, 闫志蕊1,2,3,4,6 |
| Source: |
Coal Science & Technology (0253-2336). Jun2026, Vol. 54 Issue 6, p1-22. 22p. |
| Subject Terms: |
*Detection algorithms, *Object recognition algorithms, *Optimization algorithms, *Computer vision, *Image enhancement (Imaging systems), *Coal mining safety, *Hazard mitigation |
| Geographic Terms: |
China |
| Abstract (English): |
The coal-dominated energy structure will persist for an extended period during China’s industrialization process, making the safe and efficient extraction of coal resources critically important. With the advancement of intelligent coal mine construction and machine vision technology, object detection-based intelligent safety management systems have been widely adopted in the coal industry. Among these, the YOLO series algorithms demonstrate significant advantages in underground intelligent detection tasks due to their high real-time performance and strong generalization capabilities. As the primary site for coal production, fully-mechanized mining face pose multiple challenges for the practical application of YOLO algorithms due to their complex, harsh environments and dynamic human-machine interaction scenarios. To systematically organize and advance the development of YOLO algorithms for target detection in fullymechanized mining face, this paper first explains the fundamental principles of YOLO algorithms. It systematically reviews the evolution and technical architectures of over ten typical variants, including official versions from YOLOv1 to YOLOv12 and YOLO3D, identifying three key technical evolution paths: accuracy enhancement, lightweight design, and scene adaptability. Subsequently, focusing on the unique conditions of fully-mechanized mining face, it systematically summarizes image preprocessing methods, classic public datasets, and model evaluation frameworks. Centered on the four-dimensional application requirements of "person-machine-environment-management", it thoroughly analyzes the current application status of YOLO algorithms in tasks such as monitoring unsafe human behaviors, identifying equipment status, intelligent environmental perception, and safety management assistance. It compares and analyzes the improvement strategies and field performance of different algorithm versions. Building on this foundation, we identify four major interference factors-low illumination, heavy dust, intense vibration, and dense small targets, and summarize mainstream optimization approaches including image enhancement, multimodal fusion, temporal compensation, and multi-scale feature integration. Finally, considering both the demands of intelligent coal mine development and the challenges of the complex underground environment, this paper discusses the future direction of object detection algorithms for fully-mechanized face. This work aims to provide a theoretical reference and technical support for intelligent safety monitoring and the high-quality development of intrinsically safe practices in China’s coal mines. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
以煤为主的能源结构在我国工业化发展过程中将持续相当长的一段时间, 实现煤炭资源的安 全、高效开采至关重要。随着煤矿智能化建设和机器视觉技术的发展, 基于目标检测的智能化安全 管控系统已在煤炭行业规模化应用, 其中 YOLO (You Only Look Once) 系列算法凭借其高实时性与强 泛化能力, 在井下智能检测任务中展现出显著优势。综采工作面作为煤炭生产的第一场所, 其复杂 恶劣的环境与人机交错的动态作业场景, 给 YOLO 算法的落地应用带来了多重挑战。为系统梳理并 推动 YOLO 算法在综采工作面目标检测领域的发展, 首先阐述 YOLO 算法的基本原理, 系统梳理 YOLOv1 至 YOLOv12 官方版本及 YOLO3D 等 10 余种典型变体的演进历程与技术架构, 归纳出精度 提升、轻量化设计、场景自适应 3 条技术演进路线。进而聚焦综采工作面特殊场景, 系统总结图像 预处理方法、经典公开数据集与模型评价体系, 并围绕"人−机−环−管"四维应用需求, 深入剖析 YOLO 算法在人员不安全行为监测、设备状态识别、环境智能感知及安全管理辅助等任务中的应用 现状, 对比分析各版本算法的改进策略与现场表现。在此基础上, 针对低照度、强粉尘、剧烈振动、 小目标密集四大典型干扰, 归纳出图像增强、多模态融合、时序补偿、多尺度特征融合等主流优化 技术路径。最后, 立足于煤矿智能化发展的深度需求与井下环境的极端复杂性, 对未来综采工作面 目标检测算法的发展方向进行了展望, 旨在为我国煤矿智能安全监测和智能化本质安全高质量发展 提供理论参考与技术支撑。 [ABSTRACT FROM AUTHOR] |
| Database: |
Energy & Power Source |
