文献类型：期刊文献

英文题名：Cascade CenterNet: Robust Object Detection for Power Line Surveillance

作者：Dai, Zhiyong[1,5];Yi, Jianjun[1];Jiang, Lihui[2,3];Yang, Shikun[2,3];Huang, Xiaoci[4]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[2]Shanghai Aerosp Control Technol Inst, Shanghai 201109, Peoples R China;[3]Shanghai Key Lab Aerosp Intelligent Control Techn, Shanghai 201109, Peoples R China;[4]Shanghai Univ Engn Sci, Sch Mech & Automot Engn, Shanghai 201620, Peoples R China;[5]Deepblue Acad Sci, Shanghai 200240, Peoples R China

年份：2021

卷号：9

起止页码：60244

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20211610231429);WOS:【SCI-EXPANDED(收录号:WOS:000642752300001)】；

基金：This work was supported in part by the Natural Science Fund of China (NSFC) under Grant 51575186, in part by the Shanghai Science and Technology Action Plan under Grant 18DZ1204000, Grant 18510745500, Grant 18510750100, and Grant 18510730600; in part by the Shanghai Aerospace Science and Technology Innovation Fund (SAST) under Grant 2019-080, 2019-116, and in part by the Shanghai Sailing Program under Grant 20YF1417300.

语种：英文

外文关键词：Object detection; Surveillance; Detectors; Task analysis; Deep learning; Image segmentation; Feature extraction; Convolutional neural networks; deep learning; object detection; electric transmission line

摘要：The use of object detection methods is becoming increasingly important in the internet of smart energy. Accurate detection of power line region and suspicious objects can reduce the probability of accidents and save manpower. However, the noisy outdoor background and time-varying lighting conditions bring difficulties to those tasks. To address those challenges, this paper investigates mainstream methods of power line region surveillance and proposes a new method for improving the accuracy of power line region and risk object detection by modeling the heat map branch of CenterNet, which is the most representative of anchor-free detectors, with a cascade guiding structure and an improved loss function. Finally, comprehensive experiments are conducted to compare some state-of-the-art detectors and the proposed model on power line surveillance tasks. The test results demonstrate both the effectiveness and superiority of the proposed method.