Detection and autonomous landing of insulators by light and small UAV based on deep learning

Guoqi Zeng; Junyi Ma; Guida Wang; Pengyu Liu; Huanyin Yue

doi:10.1117/12.2600356

1 June 2021 Detection and autonomous landing of insulators by light and small UAV based on deep learning

Guoqi Zeng, Junyi Ma, Guida Wang, Pengyu Liu, Huanyin Yue

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 1184815 (2021) https://doi.org/10.1117/12.2600356
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

This paper proposes and verifies a design for autonomous identification, positioning and landing of insulators on transmission lines by light and small UAVs based on deep learning. First, the improved YOLOV3 algorithm is used to identify the insulator string target.And the image augmentation technology is used to improve the recognition accuracy of the algorithm model in multiple environments. Designing an adaptive frame extraction algorithm ensures that the model can be run in real time to detect targets under limited equipment resources. We use the insulator string detection output box to calculate the offset, height and other spatial information, which is used to determine the position and heading of the UAV relative to the insulator string on the horizontal plane. According to the offset of the position and heading, the UAV is dynamically position controlled to realize the automatic landing of the UAV. Through a series of field tests, the proposed improved model algorithm obtains 84.5% MAP (mean average precision), and the actual results of autonomous landing verify the feasibility of the proposed method.

Citation Download Citation

Guoqi Zeng, Junyi Ma, Guida Wang, Pengyu Liu, and Huanyin Yue "Detection and autonomous landing of insulators by light and small UAV based on deep learning", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 1184815 (1 June 2021); https://doi.org/10.1117/12.2600356

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