融合多维空洞卷积算子和多层次特征的深度网络检测算法

doi:10.16451/j.cnki.issn1003-6059.202010004

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Abstract The exclusive usage of sequential convolution operation in the deep networks results in the lack of the target detailed information of feature layers and global characteristics. The detection performance for small objects and the detection accuracy are reduced. In this paper, a deep networks detection algorithm fusing multiple dilated convolution(MDC) operator and multi-level characteristics is proposed based on the residual network structure. The convolution kernel is composed of 5 different receptive fields and 8 different semantic feature maps can be generated. The MDC operator is introduced into the feature extraction block to build a new feature layer. The transposition convolution is employed to increase the dimension of the detection layer and make a collage of multi-level feature layers. Thus, the original features of the targets can be retained in the newly generated detection layer to the most extent. Finally, the detection model is constructed by the non-maximal suppression. The experimental results show that the proposed model with the multi-leveled features and MDC operator can effectively improve the mean average precision and detection performance for small targets.

Key words： Multiple Dilated Convolution(MDC) Operator Target Detection Transposition Convolution Detailed Information Global Information

Received: 13 July 2020

ZTFLH:

TP391

Fund:Key Scientific Research Projects of Universities in Henan Province(No.21A120004), Innovation Scientists and Technicians Troop Construction Projects of Henan Province(No.CXTD2016054), Zhongyuan High Level Talents Special Support Plan(No.ZYQR201912031), Fundamental Research Funds of Henan Polytechnic University(No.NSFRF170501)

Corresponding Authors: ZHAO Yunji, Ph.D., lecturer. His research interests include pattern recognition and intelligent control.

About author:: ZHANG Xinliang, Ph.D., associate professor. His research interests include intelligent control, detection technology and automatic equipment.XIE Heng, master student. His research interests include pattern recognition and digi-tal image processing.WANG Wanru, master student. Her research interests include pattern recognition and digital image processing.WEI Shengqiang, master student. His research interests include pattern recognition and digital image processing.

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Cite this article:

ZHANG Xinliang,XIE Heng,ZHAO Yunji等. Deep Networks Detection Algorithm Fusing Multiple Dilated Convolution Operator and Multi-level Characteristics[J]. , 2020, 33(10): 898-905.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202010004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I10/898

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