| 苏国用,帅洪锌,邓海顺,王鹏彧,赵东洋,庞子金.基于机器视觉的煤矿多场景目标检测方法研究[J].电子测量与仪器学报,2025,39(7):212-226 |
| 基于机器视觉的煤矿多场景目标检测方法研究 |
| Research on multi-scene key target detection method forcoal mine based on machine vision |
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| DOI： |
| 中文关键词: 复杂作业场景 机器视觉 目标检测 YOLO 视觉动态感知 |
| 英文关键词:complex operational scenarios machine vision target detection YOLO visual dynamic perception |
| 基金项目:安徽省高等学校科学研究项目(2022AH050834)、国家自然科学基金项目(52304166)、深部煤矿采动响应与灾害防控国家重点实验室开放基金(SKLMRDPC22KF24)、安徽理工大学矿山智能技术与装备省部共建协同创新中心开放基金(CICJMITE202206)、安徽理工大学引进人才科研启动基金(2022yjrc61)、安徽省高校优秀科研创新团队项目(2022AH010052)、安徽理工大学研究生创新基金(2025cx2056)项目资助 |
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| Author | Institution |
| Su Guoyong | 1.State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University
of Science and Technology, Huainan 232001, China; 2.Collaborative Innovation Center for Mining Intelligent
Technology and Equipment, Anhui University of Science and Technology, Huainan 232001, China; 3.School
of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China |
| Shuai Hongxin | School
of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China |
| Deng Haishun | 1.State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University
of Science and Technology, Huainan 232001, China; 2.Collaborative Innovation Center for Mining Intelligent
Technology and Equipment, Anhui University of Science and Technology, Huainan 232001, China; 3.School
of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China |
| Wang Pengyu | 2.Collaborative Innovation Center for Mining Intelligent
Technology and Equipment, Anhui University of Science and Technology, Huainan 232001, China; 3.School
of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China |
| Zhao Dongyang | School
of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China |
| Pang Zijin | School
of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China |
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| 中文摘要: |
| 针对煤矿井下复杂作业场景中高粉尘、低照度、人机多目标混杂与跨尺度变化等因素导致作业人员与装备的目标检测效果不佳问题，提出一种基于机器视觉的煤矿多场景关键目标检测方法。首先，采用CGNet (context guided network)特征提取模块、SlimNeck特征融合模块与Dyhead动态检测头对YOLOv5s算法进行优化，以构建YOLOv5s-CSD网络模型。其次，基于自建煤矿数据集，围绕YOLOv5s-CSD模型开展消融实验、对比实验与嵌入式检测实验。实验结果表明，在煤矿井下掘进、支锚、采煤与辅助运输4种复杂作业场景中，YOLOv5s-CSD的检测精度达91.0%，相较于YOLOv5s算法提升了3.5%，并且其与YOLOv9s、YOLOv11s、YOLOv12s等6种主流目标检测算法相比，模型复杂度适中且检测精度最高。在实验测试平台上，YOLOv5s-CSD模型对工作人员、支护装置、电机车等7类关键目标的实时检测精度均在90.0%以上，并且其实时检测速度达38.6 fps，检测精度高且实时性强，可为煤矿井下复杂环境的视觉动态感知提供技术支撑。 |
| 英文摘要: |
| Aiming at the problem of poor target detection of operating personnel and equipment due to high dust, low illumination, human-machine multi-target mixing and cross-scale changes in the complex operation scene of coal mine underground, we propose a multi-scene key target detection method based on machine vision for coal mine. Firstly, the YOLOv5s algorithm is optimised using CGNet (context guided network) feature extraction module, SlimNeck feature fusion module with Dyhead dynamic detection head in order to construct the YOLOv5s-CSD network model. Secondly, based on the self-constructed coal mine dataset, ablation experiments, comparison experiments and embedded detection experiments were carried out around the YOLOv5s-CSD model. The experimental results show that YOLOv5s-CSD achieves a detection accuracy of 91.0% in four complex operation scenarios of underground coal mine tunneling, anchor support, coal mining, and auxiliary transport, which is 3.5% higher than YOLOv5s algorithm, and compared with six mainstream target detection algorithms, such as YOLOv9s, YOLOv11s, and YOLOv12s, it has the moderate model complexity and the highest detection accuracy. On the experimental test platform, the real-time detection accuracy of YOLOv5s-CSD model for seven types of key targets, such as person, support, and electric locomotive, is above 90.0%, and its real-time detection speed is up to 38.6 frames/s, which is high in detection accuracy and real-time, and it can provide technical support for the visual dynamic perception of the complex environment of underground coal mines. |
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