煤矿井下行人检测算法
Pedestrian detection algorithm of coal mine underground
【索引】杨清翔,吕晨,冯晨晨,等.煤矿井下行人检测算法[J].工矿千亿国际app下载,2020,46(1):80-84.
【Reference】YANG Qingxiang,LYU Chen,FENG Chenchen,et al.Pedestrian detection algorithm of coal mine underground[J].Industry and Mine Automation,2020,46(1):80-84.
【DOI】10.13272/j.issn.1671-251x.17540
【作者】杨清翔1,吕晨2,冯晨晨2,王振宇2
【Author】 YANG Qingxiang1,LYU Chen2,FENG Chenchen2,WANG Zhenyu2
【作者机构】1.山西中煤华晋能源有限责任公司 王家岭煤矿, 山西 河津043300;2.中国矿业大学 信息与控制工程学院, 江苏 徐州221116
【Unit】1.Wangjialing Coal Mine, Shanxi Zhongmei Huajin Energy Co., Ltd., Hejin 043300, China; 2.School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
【摘要】针对井下光照不均匀、行人特征与背景的相似度高等导致基于计算机视觉的行人检测技术在井下应用面临很大挑战的问题,提出采用Faster区域卷积神经网络(RCNN)进行煤矿井下行人检测。Faster RCNN行人检测算法采用区域建议网络(RPN)生成候选区域,RPN 与Fast RCNN共享卷积层,以提高网络训练和检测速度;在图像特征提取过程中采用动态自适应池化方法对不同池化域进行自适应池化操作,提高了检测准确性。实验结果表明,该算法对于不同环境下图像中的行人均具有较好的检测效果。
【Abstract】Due to uneven underground illumination and high similarity between pedestrian characteristics and background, pedestrian detection technology based on computer vision is facing great challenges in underground application. Faster region convolutional neural networks(RCNN) was proposed for pedestrians detection of coal mine underground. Faster RCNN pedestrian detection algorithm uses region proposal network(RPN) to generate candidate regions. RPN shares convolutional layer with Fast RCNN, so as to improve network training and detection speed. A dynamic self-adaptive pooling method is adopted to perform self-adaptive pooling operation for different pooling domains in the process of image feature extraction, so as to improve detection accuracy. The experimental results show that the algorithm has better detection effect for pedestrian image in different environments.
【关键词】 井下行人检测; 深度学习; 区域卷积神经网络; 区域建议网络; 共享卷积层; 动态自适应池化
【Keywords】underground pedestrian detection; deep learning; region convolutional neural networks; region proposal network; shared convolutional layer; dynamic self-adaptive pooling
【文献出处】工矿千亿国际app下载,2020年1期
【基金】国家重点研发计划资助项目(2018YFC0808302)
【分类号】TD76
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