深度卷积应用于目标检测算法综述

doi:10.3778/j.issn.1673-9418.2111063

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (5): 1025-1042.DOI: 10.3778/j.issn.1673-9418.2111063

深度卷积应用于目标检测算法综述

董文轩, 梁宏涛(), 刘国柱, 胡强, 于旭

青岛科技大学信息科学技术学院,山东青岛 266061

收稿日期:2021-11-11 修回日期:2022-01-24 出版日期:2022-05-01 发布日期:2022-05-19
通讯作者: + E-mail: lht@qust.edu.cn
作者简介:董文轩（1997—）,男,山东德州人,硕士研究生,CCF学生会员,主要研究方向为机器学习、计算机视觉等。
梁宏涛（1979—）,男,山东济宁人,博士,副教授,CCF高级会员,主要研究方向为数据挖掘、能源互联网、计算机视觉等。
刘国柱（1965—）,男,山东青岛人,硕士,教授,主要研究方向为网络安全、大数据、计算机视觉等。
胡强（1980—）,男,山东邹城人,博士,副教授,CCF专业会员,主要研究方向为软件形式化验证、服务计算、计算机视觉等。
于旭（1982—）,男,山东青岛人,博士,副教授,CCF专业会员,主要研究方向为推荐系统、迁移学习、计算机视觉等。
基金资助:
国家自然科学基金(61973180);山东省产教融合研究生联合培养示范基地项目(2020-19)

Review of Deep Convolution Applied to Target Detection Algorithms

DONG Wenxuan, LIANG Hongtao(), LIU Guozhu, HU Qiang, YU Xu

School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong 266061, China

Received:2021-11-11 Revised:2022-01-24 Online:2022-05-01 Published:2022-05-19
About author:DONG Wenxuan, born in 1997, M.S. candidate, student member of CCF. His research interests include machine learning, computer vision, etc.
LIANG Hongtao, born in 1979, Ph.D., associate professor, senior member of CCF. His research interests include data mining, Internet of energy, computer vision, etc.
LIU Guozhu, born in 1965, M.S., professor. His research interests include network security, big data, computer vision, etc.
HU Qiang, born in 1980, Ph.D., associate professor, professional member of CCF. His research interests include software formal verification, service computing, computer vision, etc.
YU Xu, born in 1982, Ph.D., associate professor, professional member of CCF. His research interests include recomendation system, transfer learning, computer vision, etc.
Supported by:
National Natural Science Foundation of China(61973180);Industry-Education Integration Postgraduate Joint Cultivation Demonstration Base Project of Shandong Province(2020-19)

摘要/Abstract

摘要：

目标检测作为计算机视觉中最基本、最具挑战性的任务之一,旨在找出图像中特定的目标,并对目标进行定位和分类,现已被广泛应用于工业质检、视频监控、无人驾驶等众多领域。近年来,随着计算机硬件资源和深度卷积算法在图像分类任务中取得突破性进展,基于深度卷积的目标检测算法也逐渐替代了传统的目标检测算法,在精度和性能方面取得了显著成果。综述了基于深度卷积的目标检测算法的研究现状以及今后可能的发展方向。以传统目标检测算法存在的局限性为引,首先介绍了目标检测算法权威的数据集和评估指标;再以时间和算法架构为研究主线,综述了近年来基于深度卷积的目标检测代表性算法的研究和发展历程,对比分析了单阶段、双阶段以及其他改进算法的网络架构,并归纳总结出各类目标检测算法所存在的特点、优势和局限;最后结合当下目标检测存在的问题与挑战对未来趋势进行展望。

关键词: 计算机视觉, 深度卷积, 目标检测, 单阶段, 双阶段

Abstract:

As one of the most fundamental and challenging tasks in computer vision, target detection aims to find out specific targets in images and to locate and classify them, and is now widely used in many fields such as industrial quality inspection, video surveillance and unmanned vehicles. In recent years, with the breakthroughs in computer hardware resources and depth convolution algorithms in image classification tasks, depth convolution-based target detection algorithms have gradually replaced the traditional target detection algorithms and achieved significant results in terms of accuracy and performance. This paper reviews the current research status of depth convolution-based target detection algorithms and possible future development directions. It introduces the authoritative datasets and evaluation metrics of target detection algorithms with the limitations of traditional target detection algorithms as a guide, and then reviews the research and development history of representative algorithms for depth convolution-based target detection in recent years with time and algorithm architecture as the main research lines. The network structures of one-stage, two-stage and other improved algorithms are compared and analyzed, and the characteristics, advantages and limitations of various target detection algorithms are summarized. Finally, the future trends are prospected in the light of current problems and challenges of target detection.

Key words: computer vision, deep convolution, target detection, one-stage, two-stage

中图分类号:

TP301

董文轩, 梁宏涛, 刘国柱, 胡强, 于旭. 深度卷积应用于目标检测算法综述[J]. 计算机科学与探索, 2022, 16(5): 1025-1042.

DONG Wenxuan, LIANG Hongtao, LIU Guozhu, HU Qiang, YU Xu. Review of Deep Convolution Applied to Target Detection Algorithms[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(5): 1025-1042.

图/表 24

表1 VOC2012类别

Table 1 VOC2012 categories

Vehicles	Household	Animals	Other
Aeroplane	Bottle	Bird	Person
Bicycle	Chair	Cat
Boat	Dining table	Cow
Bus	Potted plant	Dog
Car	Sofa	Horse
Motorbike	TV/Monitor	Sheep
Train

表2 混淆矩阵

Table 2 Confusion matrix

预测值	真实值
预测值	正例（Positive）	负例（Negative）
正确（True）	TP	TN
错误（False）	FP	FN

图1 目标检测算法发展时间轴

Fig.1 Timeline of target detection algorithm development

图2 R-CNN模型结构

Fig.2 R-CNN model structure

图3 空间金字塔池化（SPP）

Fig.3 Spatial pyramid pooling (SPP)

图4 Fast R-CNN模型结构

Fig.4 Fast R-CNN model structure

图5 Faster R-CNN模型结构

Fig.5 Faster R-CNN model structure

图6 位置敏感分数图

Fig.6 Position-sensitive score maps

图7 Faster R-CNN与Cascade R-CNN检测架构

Fig.7 Faster R-CNN and Cascade R-CNN detection architecture

图8 平衡特征金字塔

Fig.8 Balanced semantic pyramid

表3 Two-stage目标检测算法性能对比

Table 3 Performance comparison of two-stage target detection algorithms

算法	主干网络	检测速率/（frame/s）	GPU	mAP/%
算法	主干网络	检测速率/（frame/s）	GPU	VOC2007	VOC2012	COCO(mAP@[0.50,0.95])
R-CNN	AlexNet	0.03	Titan X	58.5 (ILSVRC2012^[43]+VOC2007)	—	—
R-CNN	VGG16	0.50	Titan X	66.0 (ILSVRC2012+VOC2007)	—	—
SPPNet	ZF-5	2.00	Titan X	59.2 (ImageNet2012)	—	—
Fast R-CNN	VGG16	3.00	K40	70.0 (VOC2007+VOC2012)	68.4 (VOC2007+VOC2012)	19.7 (COCO)
Faster R-CNN	VGG16	7.00	Titan X	73.2 (VOC2007+VOC2012)	70.4 (VOC2007+VOC2012)	21.9 (COCO)
R-FCN	ResNet101	5.80	K40	79.5 (VOC2007+VOC2012)	77.6 (VOC2007+VOC2012)	29.9 (COCO)
Cascade R-CNN	ResNet101	7.00	Titan Xp	—	—	42.8 (COCO)
Libra R-CNN	ResNeXt101^[44]	—	—	—	—	43.0(COCO)

表4 Two-stage目标检测算法总体分析

Table 4 Overall analysis of two-stage target detection algorithms

算法	改进方式	优势	局限
R-CNN	CNN应用于目标检测,选择性搜索算法,SVM分类,NMS筛选	开辟深度学习在目标检测方面的应用,性能优于传统目标检测算法	效率低,存储空间需求大,各个模块之间独立,丢失原图信息
SPPNet	提出空间金字塔	减少计算量,检测速度提高,移除对网络固定尺寸的限制	存储空间需求大,训练繁琐,改进局限于全连接层
Fast R-CNN	引入ROI Pooling,softmax替代SVM	减少存储空间占用,降低计算复杂度,提高了检测精度	耗时耗空间,候选框模块独立
Faster R-CNN	引入区域建议网络,共享卷积层的特征图,提出了锚框	实现端到端的目标检测模型,减少候选框数,减少模型计算量	丧失网络平移不变性,对小目标检测较差
R-FCN	引入位置敏感分数图,对感兴趣区域进行了编码处理	使网络具有平移不变性,进一步提高检测精度	主干网络模型加深,检测速度慢
Cascade R-CNN	引入级联架构	缓解过拟合,IoU相匹配,减少网络检测噪声,提高检测准确度	增加了网络模型的复杂度,延长了网络训练和预测的时间
Libra R-CNN	引入平衡特征金字塔,采用分桶策略进行平衡采样,Balance L1替代Smooth L1	减轻模型因样本数据、特征不平衡而造成的影响,平滑训练时的梯度	网络模型更为复杂,无法满足实时的要求

图9 YOLOv1模型结构

Fig.9 YOLOv1 model structure

图10 YOLOv3模型结构

Fig.10 YOLOv3 model structure

图11 YOLOv5模型结构

Fig.11 YOLOv5 model structure

图12 YOLO系列检测头结构对比

Fig.12 Comparison of structure of YOLO serieshead detection

图13 SSD模型结构

Fig.13 SSD model structure

图14 DSSD模型结构

Fig.14 DSSD model structure

图15 FSSD模型结构

Fig.15 FSSD model structure

图16 RetinaNet模型结构

Fig.16 RetinaNet model structure

图17 CornerNet模型结构

Fig.17 CornerNet model structure

图18 EfficientDet模型结构

Fig.18 EfficientDet model structure

表5 One-stage目标检测算法性能对比

Table 5 Performance comparison of one-stage target detection algorithms

算法	主干网络	检测速率/（frame/s）	GPU	mAP/%
算法	主干网络	检测速率/（frame/s）	GPU	VOC2007	VOC2012	COCO(mAP@[0.50,0.95])
YOLOv1	VGG16	45.0	Titan X	66.4 (VOC2007+VOC2012)	57.9 (VOC2007+VOC2012)	—
YOLOv2	DarkNet19	40.0	Titan X	78.6 (VOC2007+VOC2012)	73.4 (VOC2007+VOC2012)	21.6 (COCO)
YOLOv3	DarkNet53	78.0	Titan X	—	—	33.0 (COCO)
YOLOv4	CSPDarkNet53	66.0	RTX 2070	—	—	43.5 (COCO)
YOLOv5	Focus+CSP	140.0	Tesla P100	—	—	—
YOLOX-X	Modified CSPv5	57.8	Tesla V100	—	—	51.2 (COCO)
SSD300	VGG16	46.0	Titan X	74.3 (VOC2007+VOC2012)	72.4 (VOC2007+VOC2012)	23.2 (COCO)
SSD512	VGG16	19.0	Titan X	76.8 (VOC2007+VOC2012)	74.9 (VOC2007+VOC2012)	26.8 (COCO)
RSSD300	VGG16	35.0	Titan X	78.5 (VOC2007+VOC2012)	76.4 (VOC2007+VOC2012)	—
RSSD512	VGG16	16.6	Titan X	80.8 (VOC2007+VOC2012)	—	—
DSSD321	ResNet101	9.5	Titan X	78.6 (VOC2007+VOC2012)	76.3 (VOC2007+VOC2012)	28.0 (COCO)
DSSD513	ResNet101	5.5	Titan X	81.5 (VOC2007+VOC2012)	80.0 (VOC2007+VOC2012)	33.2 (COCO)
FSSD300	VGGNet	65.8	1080Ti	82.7 (VOC2007+VOC2012+ COCO)	82.0 (VOC2007+VOC2012+ COCO)	27.1 (COCO)
FSSD512	VGGNet	35.7	1080Ti	84.5 (VOC2007+VOC2012+ COCO)	84.2 (VOC2007+VOC2012+ COCO)	31.8 (COCO)
DSOD300	DS/64-192-48-1	17.4	Titan X	77.7 (VOC2007+VOC2012)	76.3 (VOC2007+VOC2012)	29.3 (COCO)
RetinaNet	ResNet101+FPN	—	—	—	—	39.1 (COCO)
RetinaNet	ResNeXt101+FPN	5.4	Titan Xp	—	—	40.8 (COCO)
CornerNet	Hourglass104	4.1	Titan Xp	—	—	42.2 (COCO)
CenterNet	Hourglass104	7.8	Titan Xp	—	—	45.1 (COCO)
EfficientDet-D7	EfficientNet	8.2	Tesla V100	—	—	53.7 (COCO)

表6 One-stage目标检测算法总体分析

Table 6 Overall analysis of one-stage target detection algorithms

算法	改进方式	优势	局限
YOLOv1	移除候选区域操作,通过 $S × S$ 网格回归检测目标	提出首个基于回归分析的目标检测算法,大幅度提高检测速率	存在漏检问题,对小目标检测效果不佳,绝对位置训练困难
YOLOv2	引入BN操作,通过 $K - m e a n s$ 获取锚框长宽比,多尺度训练方法,加入Passthrough层	模型训练更为稳定,获取大多数锚框长宽比,特征融合,避免损失细粒度特征,提高模型鲁棒性	小目标检测召回率不高,密集群体目标检测效果差
YOLOv3	引入残差操作,进行多尺度预测,跨尺度特征融合	获取更深层次的图像特征,提高了对小目标的检测效果	检测召回率低,定位精度不佳,密集物体检测效果差
YOLOv4	引入SPP+PAN结构,提出一系列调优技巧	模型具有更大的感受野,检测精度达到同时期最优	模型的锚框长宽比只能适应大部分目标,缺少泛化性
YOLOv5	自适应的锚框计算和图片放缩,引入Focus结构和FPN+PAN+CSP结构	减少模型计算量和信息损失,提高小目标的检测效果,多模型结构,灵活性高,检测速度快	延用锚框的策略,模型计算量增加和正负样本不平衡,检测精度还有待提高
YOLOX	锚框的操作,引入预测分支解耦结构,引入SimOTA操作	降低模型计算量,缓解正负样本不平衡,改善模型的收敛速度,获得最优样本匹配方案	—
SSD	多层次特征图融合	解决了小目标难以检测的问题	特征图检测独立,计算量大
RSSD	分类网络增加不同层之间的特征图联系,网络间参数共享	改进特征融合方式,检测更多的小尺寸目标,参数共享减少计算量	模型相较复杂,计算效率较低
DSSD	引入反卷积和残差单元	对小目标检测的效果显著提升	训练时间变长,检测速度变慢
FSSD	借鉴了FPN特征融合思想	将浅层的细节特征和高层的语义特征进行融合,提高了检测精度	检测精度还有提升空间,检测速度变慢
DSOD	引入Dense Prediction结构	避免梯度消失,减少了参数数量	获取的特征冗余,增加模型的计算量
RetinaNet	ResNet和FPN相结合,应用focal loss	平衡正负样本比例,取图像多尺度特征图,使模型更注重困难样本	模型设计复杂,检测速度变慢
CornerNet	采用预测角点对边界框进行定位,移除锚框操作	缓解正负样本不均衡,减少超参数计算,对边界框定位更准确	角点分组匹配耗时较长,存在角点匹配错误
CenterNet	采用中心点对边界框进行定位,移除NMS后处理方式	计算相对简单,提高检测速度	对于目标中心重叠时,模型只能检测出单个目标
EfficientDet	提出加权双向特征金字塔网络（BiFPN）	更深层次的特征融合,BiFPN的通道数、重复层数可控,模型更灵活	模型预训练成本过高

表6 One-stage目标检测算法总体分析

Table 6 Overall analysis of one-stage target detection algorithms

算法	改进方式	优势	局限
YOLOv1	移除候选区域操作,通过 $S × S$ 网格回归检测目标	提出首个基于回归分析的目标检测算法,大幅度提高检测速率	存在漏检问题,对小目标检测效果不佳,绝对位置训练困难
YOLOv2	引入BN操作,通过 $K - m e a n s$ 获取锚框长宽比,多尺度训练方法,加入Passthrough层	模型训练更为稳定,获取大多数锚框长宽比,特征融合,避免损失细粒度特征,提高模型鲁棒性	小目标检测召回率不高,密集群体目标检测效果差
YOLOv3	引入残差操作,进行多尺度预测,跨尺度特征融合	获取更深层次的图像特征,提高了对小目标的检测效果	检测召回率低,定位精度不佳,密集物体检测效果差
YOLOv4	引入SPP+PAN结构,提出一系列调优技巧	模型具有更大的感受野,检测精度达到同时期最优	模型的锚框长宽比只能适应大部分目标,缺少泛化性
YOLOv5	自适应的锚框计算和图片放缩,引入Focus结构和FPN+PAN+CSP结构	减少模型计算量和信息损失,提高小目标的检测效果,多模型结构,灵活性高,检测速度快	延用锚框的策略,模型计算量增加和正负样本不平衡,检测精度还有待提高
YOLOX	锚框的操作,引入预测分支解耦结构,引入SimOTA操作	降低模型计算量,缓解正负样本不平衡,改善模型的收敛速度,获得最优样本匹配方案	—
SSD	多层次特征图融合	解决了小目标难以检测的问题	特征图检测独立,计算量大
RSSD	分类网络增加不同层之间的特征图联系,网络间参数共享	改进特征融合方式,检测更多的小尺寸目标,参数共享减少计算量	模型相较复杂,计算效率较低
DSSD	引入反卷积和残差单元	对小目标检测的效果显著提升	训练时间变长,检测速度变慢
FSSD	借鉴了FPN特征融合思想	将浅层的细节特征和高层的语义特征进行融合,提高了检测精度	检测精度还有提升空间,检测速度变慢
DSOD	引入Dense Prediction结构	避免梯度消失,减少了参数数量	获取的特征冗余,增加模型的计算量
RetinaNet	ResNet和FPN相结合,应用focal loss	平衡正负样本比例,取图像多尺度特征图,使模型更注重困难样本	模型设计复杂,检测速度变慢
CornerNet	采用预测角点对边界框进行定位,移除锚框操作	缓解正负样本不均衡,减少超参数计算,对边界框定位更准确	角点分组匹配耗时较长,存在角点匹配错误
CenterNet	采用中心点对边界框进行定位,移除NMS后处理方式	计算相对简单,提高检测速度	对于目标中心重叠时,模型只能检测出单个目标
EfficientDet	提出加权双向特征金字塔网络（BiFPN）	更深层次的特征融合,BiFPN的通道数、重复层数可控,模型更灵活	模型预训练成本过高

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深度卷积应用于目标检测算法综述

Review of Deep Convolution Applied to Target Detection Algorithms

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