Multi-Object Detection Algorithm in Wind Turbine Nacelles Based on Improved YOLOX-Nano

Chunsheng Hu, Yong Zhao (), Fangjuan Cheng and Zhiping Li
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Chunsheng Hu: School of Mechanical Engineering, Ningxia University, Yinchuan 750000, China
Yong Zhao: School of Mechanical Engineering, Ningxia University, Yinchuan 750000, China
Fangjuan Cheng: School of Mechanical Engineering, Ningxia University, Yinchuan 750000, China
Zhiping Li: School of Mechanical Engineering, Ningxia University, Yinchuan 750000, China

Energies, 2023, vol. 16, issue 3, 1-13

Abstract: With more and more wind turbines coming into operation, inspecting wind farms has become a challenging task. Currently, the inspection robot has been applied to inspect some essential parts of the wind turbine nacelle. The detection of multiple objects in the wind turbine nacelle is a prerequisite for the condition monitoring of some essential parts of the nacelle by the inspection robot. In this paper, we improve the original YOLOX-Nano model base on the short monitoring time of the inspected object by the inspection robot and the slow inference speed of the original YOLOX-Nano. The accuracy and inference speed of the improved YOLOX-Nano model are enhanced, and especially, the inference speed of the model is improved by 72.8%, and it performs better than other lightweight network models on embedded devices. The improved YOLOX-Nano greatly satisfies the need for a high-precision, low-latency algorithm for multi-object detection in wind turbine nacelle.

Keywords: wind turbine nacelle; multiple objects; improved YOLOX-Nano; inference speed; inspection robot (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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