Fu-Tian Wang, Li Yang, Jin Tang, Si-Bao Chen, Xin Wang. DLA+: A Light Aggregation Network for Object Classification and Detection. International Journal of Automation and Computing. https://doi.org/10.1007/s11633-021-1287-y
Citation: Fu-Tian Wang, Li Yang, Jin Tang, Si-Bao Chen, Xin Wang.

DLA+: A Light Aggregation Network for Object Classification and Detection

. International Journal of Automation and Computing. https://doi.org/10.1007/s11633-021-1287-y

DLA+: A Light Aggregation Network for Object Classification and Detection

doi: 10.1007/s11633-021-1287-y
More Information
  • Author Bio:

    Fu-Tian Wang received the B. Eng. degree in computer science and technology, the M. Eng. degree in computer application and the Ph. D. degree in computer software and theory from Anhui University, China in 2005, 2009 and 2017 respectively. He has been a teacher in Anhui University, China from 2009. His research interests include image processing, computer vision and edge computing.E-mail: wft@ahu.edu.cnORCID iD: 0000-0003-4181-8485

    Li Yang received the B. Eng. degree in electricalengineering and automation from Luoyang Institute of Technology, China in 2017. He is currently a master student in computer science and technology, Anhui University, China. His research interests include computer vision, object detection and model compression.E-mail: 543320032@qq.com

    Jin Tang received the B. Eng. degree in automation and the Ph. D. degree in computer science from Anhui University, China in 1999 and 2007, respectively. He is currently a professor with School of Computer Science and Technology, Anhui University, China. His research interests include computer vision, pattern recognition, machine learning and deep learning.E-mail: tangjin@ahu.edu.cn

    Si-Bao Chen received the B. Sc. and M. Sc. degrees in probability and statistics and the Ph.D. degree in computer science from Anhui University, China in 2000, 2003 and 2006, respectively. From 2006 to 2008, he was a postdoctoral researcher at Department of Electronic Engineering and Information Science, University of Science and Technology of China. From 2008, he has been a teacher in Anhui University. He was a visiting scholar at University of Texas at Arlington, USA from 2014 to 2015. His research interests include image processing, pattern recognition, machine learning and computer vision.E-mail: sbchen@ahu.edu.cn

    Xin Wang received the B. Sc. degree from Department of Precision Machinery and Precision Instruments, University of Science and Technology, China in 1998. Now, she′s the technical director of Shenzhen Raixun Information Technology Co., Ltd., and the Researcher of Peking University Shenzhen Institute, China. Her research interests include multimedia information processing, speech recognition and Internet Security. E-mail: wangxin@imsl.org.cn (Corresponding author)ORCID iD: 0000-0001-7042-2637

  • Received Date: 2020-10-24
  • Accepted Date: 2021-02-01
  • An efficient convolution neural network (CNN) plays a crucial role in various visual tasks like object classification or detection, etc. The most common way to construct a CNN is stacking the same convolution block or complex connection. These approaches may be efficient but the parameter size and computation (Comp) have explosive growth. So we present a novel architecture called “DLA+”, which could obtain the feature from the different stages, and by the newly designed convolution block, could achieve better accuracy, while also dropping the computation six times compared to the baseline. We design some experiments about classification and object detection. On the CIFAR10 and VOC data-sets, we get better precision and faster speed than other architecture. The lightweight network even allows us to deploy to some low-performance device like drone, laptop, etc.

     

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