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计算机系统应用:2020,29(1):203-208
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基于DE-YOLO的室内人员检测方法
(1.福建师范大学 光电与信息工程学院, 福州 350007;2.福建师范大学 医学光电科学与技术教育部重点实验室, 福州 350007;3.福建师范大学 福建省光子技术重点实验室, 福州 350007;4.福建师范大学 福建省光电传感应用工程技术研究中心, 福州 350007)
Indoor Personnels Detection Method Based on DE-YOLO
(1.College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China;2.Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou 350007, China;3.Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China;4.Fujian Provincial Engineering Research Center for Optoelectronic Sensors and Intelligent Information, Fuzhou 350007, China)
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投稿时间:2019-06-27    修订日期:2019-07-16
中文摘要: 目标检测的一个重要应用场景是对室内流动人员的检测与定位,为了降低模型的冗余度和提高检测的精确度,因此本文提出一种基于DE-YOLO的室内人员检测方法.通过使用K-means算法对数据集进行聚类,并设计出这种DE-YOLO深度卷积神经网络结构.通过DE-YOLO网络结构中的密集型连接,实现模型大小的压缩和特征信息的复用,最后对提取到的特征进行目标检测.在VOC2012数据集上进行实验表明,新改进的深度卷积网络应用性能有较大的提升.
Abstract:An important application scenario for target detection is the detection and location of indoor mobile personnel. In this study, we propose an indoor personnel detection method to improve YOLOv3. First, the proposed method clusters the dataset by using K-means algorithm and designs a DE-YOLO deep convolutional neural network structure. Through the dense connection in the DE-YOLO network structure, the compression of the model sizes and the reuse of the feature information are realized. Finally, the target detection is performed on the extracted features. Experiments show that the application of the newly improved deep convolutional network has greatly improved application effect on VOC2012 datasets.
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基金项目:福建省自然科学基金(2017J01744)
引用文本:
张明伟,蔡坚勇,李科,程玉,曾远强.基于DE-YOLO的室内人员检测方法.计算机系统应用,2020,29(1):203-208
ZHANG Ming-Wei,CAI Jian-Yong,LI Ke,CHENG Yu,ZENG Yuan-Qiang.Indoor Personnels Detection Method Based on DE-YOLO.COMPUTER SYSTEMS APPLICATIONS,2020,29(1):203-208

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