基于深度学习的图像目标检测算法综述

doi:10.11959/j.issn.1000-0801.2020199

Abstract

Abstract:

Image object detection is to find out the objects of interest in the image and determine their classifications and locations.It is a research hotspot in the field of computer vision.In recent years,due to the significant improvement in the accuracy of image classification with deep learning,image object detection models based on deep learning have gradually became mainstream.Firstly,the convolutional neural networks commonly used in image object detection were briefly introduced.Then,the existing classical image object detection models were reviewed from the perspective of candidate regions,regression and anchor-free methods.Finally,according to the detection results on the public dataset,the advantages and disadvantages of the models were analyzed,the problems in the image object detection research were summarized and the future development was forecasted.

Key words: computer vision, image object detection, deep learning, image classification

CLC Number:

TP393

Tingting ZHANG,Jianwu ZHANG,Chunsheng GUO,Huahua CHEN,Di ZHOU,Yansong WANG,Aihua XU. A survey of image object detection algorithm based on deep learning[J]. Telecommunications Science, 2020, 36(7): 92-106.

Figures/Tables 9

检测方法	模型	创新点	优缺点
基于候选区域	R-CNN^[6]	用CNN提取特征	用卷积网络提取特征；但特征提取复杂，耗时长
	SPP-Net^[20]	输入整张图片提取特征，并共享特征图	实现多尺度卷积计算；但占用较大硬件资源
	fast R-CNN^[22]	用ROI pooling层提取特征	初步实现端对端检测；但依赖传统方法生成候选区域
	faster R-CNN^[24]	提出区域生成网络（RPN）	实现实时检测；但对小目标检测效果不佳
	mask R-CNN^[25]	增加了用于分割任务的分支	减弱类别间的竞争优势；但与faster R-CNN相比，检测速度较慢
基于回归	?OLO^[28]	将图像空间划分为网格单元	检测速度快；但定位不准确，对密集物体检测效果不佳
	?OLOv2^[29]	使用聚类产生锚框	减少定位错误，提高分类精度；但准确率还不够高
	?OLO9000^[29]	提出了目标分类和检测的联合训练方法	为跨数据集训练提供思路；但准确率还不够高
	?OLOv3^[32]	借鉴残差学习思想，并进行多尺度检测	检测精度高并且速度是其他模型的3～4倍；但对边界框预测严格的情况下检测精度略低
	SSD^[33]	生成多尺度的锚框对边界框空间离散化	检测速度快；但准确率低，对小目标检测效果不佳
	DSSD^[34]	将ResNet-101作为骨干网，并添加反卷积层	提高小目标检测效果；但与SSD相比检测速度较慢
	RSSD^[35]	改进特征融合方式	检测精度提高；但与SSD相比检测速度较慢
	FSSD^[36]	重构金字塔特征图以融合不同尺度特征	有利于小目标检测；但与SSD相比检测速度较慢
	ASSD^[37]	融合了注意力机制的思想	检测精度提高；但与SSD相比检测速度较慢
	RetinaNet^[39]	重塑交叉熵损失，用焦点损失解决类不平衡问题	检测精度提高；但检测速度一般
基于anchor-free	CornerNet^[42]	将物体检测为两个角点，无须生成锚框	检测精度提高；但容易产生错误目标框，检测速度一般
	ExtremeNet^[47]	通过预测极值点和中心点来检测目标	于 CornerNet 相比，对小目标检测效果更好；但对大目标检测效果略差
	CenterNet^[51]	将物体检测为两个角点和一个中心点	解决容易产生错误目标框问题；但检测速度一般
	CenterNet^[52]	直接判断像素点是否为目标的中心点	算法更简洁；但与同名CenterNet相比，检测精度有所降低
	FSAF^[53]	提出动态选择特征的方式	摒弃了传统基于锚框选择合适特征层；但在anchor-free分支中引入了较多超参数
	FCOS^[54]	针对每个像素点进行预测	可以与其他视觉任务相结合；但在检测时由于真实框重叠，可能会出现语义模糊情况
	FoveaBox^[59]	对真实框进行位置变换，增加正负样本的识别度	有效地解决了正负样本不平衡问题；与其他anchor-free模型相比，检测精度略低

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检测方法	模型	骨干网	2007	2012	MS COCO		FPS
检测方法	模型	骨干网	2007	2012	AP@0.5	AP@[0.5,0.95]	FPS
基于候选区域	R-CNN^[6]	AlexNet^[9]	58.5%	53.3%	—	—	0.02
	SPP-Net^[21]	ZF-5^[9]	59.2%	—	—	—	0.5
	fast R-CNN^[22]	VGG-16^[11]	66.9%	66.0%	35.9%	19.7%	0.5
	faster R-CNN^[24]	VGG-16	73.2%	70.4%	42.7%	21.9%	7
	mask R-CNN^[25]	ResNet-101-FPN^[38]	—	—	58.0%	35.7%	5
基于回归	?OLO^[28]	VGG-16	66.4%	57.9%	—	—	45
	?OLOv2^[28]	DarkNet-19^[28]	78.6%	73.5%	44.0%	21.6%	40
	?OLOv3^[32]	DarkNet-53^[32]	—	—	57.9%	33.0%	19.6
	SSD^[33]	VGG-16	79.8%	78.5%	48.5%	28.8%	19
	DSSD^[34]	ResNet-101^[12]	81.5%	80.0%	53.3%	33.2%	5.5
	RSSD^[35]	VGG-16	80.8%	—	—	—	16.6
	FSSD^[36]	VGG-16	80.9%	84.2%	52.8%	31.8%	35.7
	ASSD^[37]	ResNet-101	83%	81.3%	55.5%	34.5%	2.7
	RetinaNet^[39]	ResNet-101-FPN	—	—	59.1%	39.1%	8.2
基于anchorfree	CornerNet^[42]	Hourglass^[15]	—	—	57.8%	42.1%	4.1
	ExtremeNet^[47]	Hourglass	—	—	60.5%	43.7%	3.1
	CenterNet^[51]	Hourglass	—	—	64.5%	47.0%	3.7
	CenterNet^[52]	Hourglass	—	—	63.9%	45.1%	7.8
	FSAF^[53]	ResNet-101	—	—	63.1%	42.8%	—
	FCOS^[54]	ResNet-101-FPN	—	—	60.7%	41.5%	—
	FoveaBox^[59]	ResNet-101	—	—	60.1%	40.6%	—

A survey of image object detection algorithm based on deep learning

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