基于改进的 YOLO 检测和双边分割网络的煤岩界面实时识别技术

IF 8.2 1区 工程技术 Q1 ENGINEERING, CIVIL
Shuzhan Xu , Wanming Jiang , Quansheng Liu , Hongsheng Wang , Jun Zhang , Jinlong Li , Xing Huang , Yin Bo
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引用次数: 0

摘要

为了提高煤岩界面识别的准确性和效率,本研究提出了一种基于实时检测算法 "只看一次(YOLO)"和轻量级双边分割网络的模型。同时,还引入了区域相似性变换函数和蜻蜓算法,以提高煤岩图像的质量。与其他三种模型的比较结果表明,所提出的模型具有卓越的边缘推断性能,在交集大于联合(IoU)阈值为 0.50 时,平均精度(mAP)为 90.2(mAP50),在 IoU 阈值为 0.50 到 0.95 的范围内,平均精度(mAP[50,95])为 81.4。此外,为了保持高精确度和实时识别能力,还使用开放式视觉推理和神经网络优化工具包对所提出的模型进行了优化,使平均每秒帧数提高了 144.97%。在四个实际煤炭工作面的实验结果证实了所提模型的有效性,表明在煤岩图像识别的准确性和效率之间取得了更好的平衡,从而为煤矿智能化的进一步发展提供了支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Coal-rock interface real-time recognition based on the improved YOLO detection and bilateral segmentation network
To improve the accuracy and efficiency of coal-rock interface recognition, this study proposes a model built on the real-time detection algorithm, you only look once (YOLO), and the lightweight bilateral segmentation network. Simultaneously, the regional similarity transformation function and dragonfly algorithm are introduced to enhance the quality of coal-rock images. The comparison with three other models demonstrates the superior edge inference performance of the proposed model, achieving a mean Average Precision (mAP) of 90.2 at the Intersection over Union (IoU) threshold of 0.50 (mAP50) and 81.4 across a range of IoU thresholds from 0.50 to 0.95 (mAP[50,95]). Furthermore, to maintain high accuracy and real-time recognition capabilities, the proposed model is optimized using the open visual inference and neural network optimization toolkit, resulting in a 144.97% increase in the mean frames per second. Experimental results on four actual coal faces confirm the efficacy of the proposed model, showing a better balance between accuracy and efficiency in coal-rock image recognition, which supports further advancements in coal mining intelligence.
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来源期刊
Underground Space
Underground Space ENGINEERING, CIVIL-
CiteScore
10.20
自引率
14.10%
发文量
71
审稿时长
63 days
期刊介绍: Underground Space is an open access international journal without article processing charges (APC) committed to serving as a scientific forum for researchers and practitioners in the field of underground engineering. The journal welcomes manuscripts that deal with original theories, methods, technologies, and important applications throughout the life-cycle of underground projects, including planning, design, operation and maintenance, disaster prevention, and demolition. The journal is particularly interested in manuscripts related to the latest development of smart underground engineering from the perspectives of resilience, resources saving, environmental friendliness, humanity, and artificial intelligence. The manuscripts are expected to have significant innovation and potential impact in the field of underground engineering, and should have clear association with or application in underground projects.
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