改进R-FCN模型的小尺度行人检测  

Small-scale pedestrian detection based on improved R-FCN model

作　　者：刘万军[1] 董利兵 曲海成[1] Liu Wanjun;Dong Libing;Qu Haicheng(School of Software,Liaoning Technical University,Huludao 125105,China)

机构地区：[1]辽宁工程技术大学软件学院,葫芦岛125105

出　　处：《中国图象图形学报》2021年第10期2400-2410,共11页Journal of Image and Graphics

基　　金：国家自然科学基金项目(41701479);辽宁省自然科学基金项目(20180550529)。

摘　　要：目的为了有效解决传统行人检测算法在分辨率低、行人尺寸较小等情境下检测精度低的问题,将基于区域全卷积网络(region-based fully convolutional networks,R-FCN)的目标检测算法引入到行人检测中,提出一种改进R-FCN模型的小尺度行人检测算法。方法为了使特征提取更加准确,在Res Net-101的conv5阶段中嵌入可变形卷积层,扩大特征图的感受野;为提高小尺寸行人检测精度,在Res Net-101中增加另一条检测路径,对不同尺寸大小的特征图进行感兴趣区域池化;为解决小尺寸行人检测中的误检问题,利用自举策略的非极大值抑制算法代替传统的非极大值抑制算法。结果在基准数据集Caltech上进行评估,实验表明,改进的R-FCN算法与具有代表性的单阶段检测器(single shot multi Box detector,SSD)算法和两阶段检测器中的Faster R-CNN(region convolutional neural network)算法相比,检测精度分别提高了3.29%和2.78%;在相同ResNet-101基础网络下,检测精度比原始R-FCN算法提高了12.10%。结论本文提出的改进R-FCN模型,使小尺寸行人检测精度更加准确。相比原始模型,改进的R-FCN模型对行人检测的精确率和召回率有更好的平衡能力,在保证精确率的同时,具有更大的召回率。Objective Pedestrian detection is a research hotspot in the field of image processing and computer vision,and it is widely used in fields such as automatic driving,intelligent monitoring,and intelligent robots.The traditional pedestrian detection method based on background modeling and machine learning can obtain a better pedestrian detection rate under certain conditions,but it cannot meet the requirements of practical applications.As deep convolutional neural networks have made great progress in general object detection,more and more scholars have improved the general object detection framework and introduced it to pedestrian detection.Compared with traditional methods,the accuracy and robustness of pedestrian detection based on deep learning methods have been improved significantly,and many breakthroughs have been made.However,the detection effect for small-scale pedestrians is not ideal.This is mainly due to a series of convolution pool operations of the convolutional neural network,which makes the feature map of small-scale pedestrians smaller,have a lower resolution,and lose serious information,leading to detection failure.To effectively solve the problem of low detection accuracy of traditional pedestrian detection algorithms in the context of low resolution and small pedestrian size,an object detection algorithm called region-based fully convolutional network(R-FCN)is introduced into pedestrian detection.This study proposes an improved small-scale pedestrian detection algorithm for R-FCN.Method The method in this study inherits the advantage of R-FCN,which employs the region proposal network to generate candidate regions of interest and position-sensitive score maps to classify and locate targets.At the same time,because the new residual network(ResNet-101)has less calculation,few parameters,and good accuracy,this study uses the Res Net-101 network as the basic network.Compared with the original R-FCN,this study mainly has the following improvements:Considering that the pedestrians in the Caltech dataset

关 键 词：行人检测 区域全卷积网络(R-FCN) 可变形卷积 多路径 非极大值抑制(NMS) Caltech数据集 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]