Nest Detection Using Coarse-to-Fine Searching Strategy

  • Nianwang Wan
  • Zhenyu DuanEmail author
  • Yongdong Hua
  • Qingbin Wang
  • Weihai Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 815)


Nest on pylon is a great threat to the safety and function of electric power system. Thus detection of nest has been considered as a vital task when checking transmission line through intelligent surveillance system. Traditional detection methods for nests are basically based on artificial identification, resulting a lot of time consumption and human waste. In addition, it’s impractical to apply this method to the situation with massive pending data. In this paper, we propose an automatic framework for nest detection. This work firstly locates the pylon, then detects nest on it through color analysis. To boost the speed of pylon location, our work combines the technology of detection and tracking after extracting HOG features of pylons. In addition, building on the observation that most pylons are standing out from background for the sake of their sharp outlines and unique color patterns, a number of filtering operations in this work are designed to significantly decrease the number of candidate boxes. Experiments have been conducted to show that this framework could achieve promisingly precise and robust detection for nest in complex environment.


Nest detection Tracking Computer vision 



The work was supported by State Key Research and Development Program (2016YFB1001003). This work was also supported by NSFC (U1611461, 61502301, 61527804, 61671298) and STCSM17511105401, China’s Thousand Youth Talents Plan, the 111 Project under Grant B07022, and the Shanghai Key Laboratory of Digital Media Processing and Transmissions.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Nianwang Wan
    • 1
  • Zhenyu Duan
    • 2
    Email author
  • Yongdong Hua
    • 1
  • Qingbin Wang
    • 3
  • Weihai Li
    • 4
  2. 2.Shanghai Jiao Tong UniversityShanghaiChina
  3. 3.Yunfu Power Supply Bureau of Guangdong Power Grid Co., Ltd.YunfuChina
  4. 4.91998 troopBinzhouChina

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