Small Object Detection Algorithm Based on Weighted Network

doi:10.3778/j.issn.1673-9418.2101040

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (9): 2143-2150.DOI: 10.3778/j.issn.1673-9418.2101040

• Graphics and Image • Previous Articles Next Articles

Small Object Detection Algorithm Based on Weighted Network

CHEN Haoran¹, PENG Li¹^,⁺(), LI Wentao¹, DAI Feifei²

1. Engineering Research Center of Internet of Things Technology Applications (School of Internet of Things Enginee-ring, Jiangnan University), Ministry of Education, Wuxi, Jiangsu 214122, China
2. Taizhou Product Quality and Safety Monitoring Institute, Taizhou, Zhejiang 318000, China

Received:2021-01-11 Revised:2021-03-10 Online:2022-09-01 Published:2021-03-29
About author:CHEN Haoran, born in 1995, M.S. candidate. His research interests include small object detection, data augmentation and deep learning.
PENG Li, born in 1967, Ph.D., professor, Ph.D. supervisor, member of CAAI and CCF. His research interests include visual Internet of things, action recognition and deep learning.
LI Wentao, born in 1996, M.S. candidate. His research interests include deep learning and computer vision.
DAI Feifei, born in 1988, M.S., engineer. Her research interests include big data and visual Internet of things.
Supported by:
National Natural Science Foundation of China(61873112);Research Fund Project of Ministry of Education-China Mobile(MCM20170204);National Key Research and Development Program of China(2018YFD0400902)

加权网络下的小目标检测算法

陈灏然¹, 彭力¹^,⁺(), 李文涛¹, 戴菲菲²

1.物联网技术应用教育部工程研究中心（江南大学物联网工程学院）,江苏无锡 214122
2.台州市产品质量安全监测研究院,浙江台州 318000

通讯作者: + E-mail: jnpengli@outlook.com
作者简介:陈灏然（1995—）,男,江苏盐城人,硕士研究生,主要研究方向为小目标检测、数据增强、深度学习。
彭力（1967—）,男,河北唐山人,博士,教授,博士生导师,CAAI会员,CCF会员,主要研究方向为视觉物联网、行为识别、深度学习。
李文涛（1996—）,男,安徽合肥人,硕士研究生,主要研究方向为深度学习、计算机视觉。
戴菲菲（1988—）,女,浙江临海人,硕士,工程师,主要研究方向为大数据、视觉物联网。
基金资助:
国家自然科学基金(61873112);教育部-中国移动科研基金项目(MCM20170204);国家重点研发计划(2018YFD0400902)

Abstract

Abstract:

For the observation of a picture, people may instinctly pay more attention to the eye-catching objects in the picture. Usually such objects tend to occupy a larger proportion in the picture, which leads to small targets being ignored. Because the area where the small target is located is often a weak detection area, and the features that can be extracted in the process of extracting features by the detector are few and are easily lost in the process of feature information transmission after the feature is extracted, the effect of small target detection is not good. Therefore, on the basis of the single-order detector, this paper adds a cross-channel interaction mechanism to ensure the integrity of the information between layers, adopts target enhancement of training samples and designs a general loss function. Apart from this, this paper improves the sample weighting on the basis of the loss function to predict weight of samples. The mAP of this paper framework UWN (unified weighted network) on the VOC public dataset is 81.2% and the mAP on the self-made small target aerial photography dataset is 82.3%. Compared with the FSSD algorithm, some speed is sacrificed, and the accuracy is greatly improved.

Key words: small target detection, target enhancement, cross-channel interaction, weighted network

摘要：

对于一幅图的观察,本能上会更多关注这幅图中相对更醒目的对象。通常这类对象会在这幅图中占据较大比重,从而导致小目标被忽视。由于小目标所在区域往往为弱测区域,检测器提取特征的过程中能够提取的特征较少,且在提取完特征后在特征信息传递的过程中容易丢失,使得针对小目标检测的效果并不是很好。因此,在单阶检测器的基础上,加入了跨信道交互的机制确保层间信息的完整,同时采取对训练样本进行目标增强并且设计了一个通用的损失函数,在此基础上改进样本加权网络预测样本的任务权重。提出的框架UWN在VOC公开数据集上的mAP为81.2%,在自制的小目标航拍数据集的mAP为82.3%。相对于FSSD算法,牺牲了部分速度,得到了精度方面的较大提升。

关键词: 小目标检测, 目标增强, 跨信道交互, 加权网络

CLC Number:

TP391.4

CHEN Haoran, PENG Li, LI Wentao, DAI Feifei. Small Object Detection Algorithm Based on Weighted Network[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(9): 2143-2150.

陈灏然, 彭力, 李文涛, 戴菲菲. 加权网络下的小目标检测算法[J]. 计算机科学与探索, 2022, 16(9): 2143-2150.

Figures/Tables 14

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Method	Fusion-layer	Enhancement?	k	mAP/%	FPS（1080Ti）
ele-sum	all	√	3	78.2	23
concat	all	√	3	77.3	28
ele-sum	Conv4-fc7-Conv7	√	3	81.2	79
ele-sum	Conv4-fc7-Conv7	×	3	80.3	77
ele-sum	Conv4-fc7-Conv7	√	5	79.8	67
ele-sum	Conv4-fc7-Conv7	×	5	77.5	60
ele-sum	Conv4-fc7-Conv7	√	7	79.8	62
ele-sum	Conv4-fc7-Conv7	×	7	77.5	57

Method	Fusion-layer	Enhancement?	k	mAP/%	FPS（1080Ti）
ele-sum	all	√	3	78.2	23
concat	all	√	3	77.3	28
ele-sum	Conv4-fc7-Conv7	√	3	81.2	79
ele-sum	Conv4-fc7-Conv7	×	3	80.3	77
ele-sum	Conv4-fc7-Conv7	√	5	79.8	67
ele-sum	Conv4-fc7-Conv7	×	5	77.5	60
ele-sum	Conv4-fc7-Conv7	√	7	79.8	62
ele-sum	Conv4-fc7-Conv7	×	7	77.5	57

Algorithm	Change focal loss?	mAP/%
UWN	√	81.2
UWN	×	80.9

Algorithm	Change focal loss?	mAP/%
UWN	√	81.2
UWN	×	80.9

Method	Backbone	Train	mAP/%	FPS(1080Ti)
Faster R-CNN	ResNet-101	VOC2007+2012	73.2	7
YOLO	VGG-16	VOC2007+2012	66.4	96
YOLOv2	DarkNet-19	VOC2007+2012	78.6	80
SSD	VGG-16	VOC2007+2012	77.2	120
DSSD	ResNet-101	VOC2007+2012	78.6	10
RFB-Net	VGG-16	VOC2007+2012	80.5	75
UWN	VGG-16	VOC2007+2012	81.2	68
UWN	VGG-16	Aerial photography	82.3	66

Small Object Detection Algorithm Based on Weighted Network

加权网络下的小目标检测算法

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Figures/Tables 14

References 23

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Metrics

Data	Method	mAP/%
VOC2007+2012	扭曲长宽	78.2
	翻转	78.4
	扭曲色域	78.8
	扭曲长宽+翻转	79.2
	扭曲长宽+扭曲色域	79.6
	扭曲色域+翻转	80.3
	本文方式	81.2