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. 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)


陈灏然1, 彭力1,+(), 李文涛1, 戴菲菲2   

  1. 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:
  • 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。
  • 基金资助:


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



关键词: 小目标检测, 目标增强, 跨信道交互, 加权网络

CLC Number: