TY - GEN
T1 - Power line detection via background noise removal
AU - Pan, Chaofeng
AU - Cao, Xianbin
AU - Wu, Dapeng
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2017/4/19
Y1 - 2017/4/19
N2 - Tiny target detections, especially power line detection, have received great attention due to its critical role in ensuring the flight safety of low-flying unmanned aerial vehicles (UAVs). In this paper, an accurate and robust power line detection method is proposed, wherein background noise is mitigated by an embedded convolution neural network (CNN) classifier before conducting the final power line extractions. Our proposed method operates in three steps: 1) extract edge features of power lines from a testing image, 2) employ a CNN classifier to remove the background noise, 3) use a Hough-Transform (HT) based fine-selection module to locate power lines. Comprehensive experiments demonstrate the superiority of the proposed method, compared to the state-of-the-art methods.
AB - Tiny target detections, especially power line detection, have received great attention due to its critical role in ensuring the flight safety of low-flying unmanned aerial vehicles (UAVs). In this paper, an accurate and robust power line detection method is proposed, wherein background noise is mitigated by an embedded convolution neural network (CNN) classifier before conducting the final power line extractions. Our proposed method operates in three steps: 1) extract edge features of power lines from a testing image, 2) employ a CNN classifier to remove the background noise, 3) use a Hough-Transform (HT) based fine-selection module to locate power lines. Comprehensive experiments demonstrate the superiority of the proposed method, compared to the state-of-the-art methods.
KW - Classification
KW - Edge feature
KW - Image processing
KW - Machine learning
KW - Power line detection
UR - https://www.scopus.com/pages/publications/85019225516
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85019225516&origin=recordpage
U2 - 10.1109/GlobalSIP.2016.7905967
DO - 10.1109/GlobalSIP.2016.7905967
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781509045457
T3 - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
SP - 871
EP - 875
BT - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PB - IEEE
T2 - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Y2 - 7 December 2016 through 9 December 2016
ER -