Research on intelligent monitoring method of external damage around transmission line based on millimeter wave radar and camera

Sihang Zhang; Zhi Yang; Chenyu Zhang; Jun Zhang; Danyu Li; Nailong Zhang; Bin Zhao; Mengxuan Li; Yi Liu; Binbin Zhao; Chang Liu

doi:10.1117/12.2681296

8 June 2023 Research on intelligent monitoring method of external damage around transmission line based on millimeter wave radar and camera

Sihang Zhang, Zhi Yang, Chenyu Zhang, Jun Zhang, Danyu Li, Nailong Zhang, Bin Zhao, Mengxuan Li, Yi Liu, Binbin Zhao, Chang Liu

Author Affiliations +

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127070A (2023) https://doi.org/10.1117/12.2681296
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

In recent years, due to the shortage of transmission line channel resources, power companies have begun to expand the capacity of existing important transmission lines. The safe and stable operation of important transmission lines is critical to the power supply status of users. Timely monitoring of potential hazards of construction machinery near the transmission lines has become a hot topic of transmission line protection against external damage. Aiming at the technical bottleneck of high false alarm rate of the current transmission line using online monitoring camera, this paper proposes a construction vehicle detection method based on the fusion of radar and visual features, which uses the physical features and geometric features of the target. The physical features such as velocity and acceleration are selected from radar. After the fusion of the radar data and camera data, the region of interest (ROI) of the radar target on the image is obtained, and the gradient direction histogram feature is extracted on the ROI. The visual features are calculated by the statistical features of gradient direction histogram, including standard deviation, median and average. This paper constructs a neural network R-V-DenseNet whose input is the fusion feature of radar and vision. Then a data set is made to train the network. The experimental results on the test set prove that the accuracy of R-V-DenseNet is improved compared with the traditional HOG-SVM method and the single sensor based detection method, which means the proposed method gains more accurate detection.

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Sihang Zhang, Zhi Yang, Chenyu Zhang, Jun Zhang, Danyu Li, Nailong Zhang, Bin Zhao, Mengxuan Li, Yi Liu, Binbin Zhao, and Chang Liu "Research on intelligent monitoring method of external damage around transmission line based on millimeter wave radar and camera", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127070A (8 June 2023); https://doi.org/10.1117/12.2681296

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KEYWORDS

Radar

Cameras

Radar sensor technology

Target detection

Detection and tracking algorithms

Feature fusion

Histograms of oriented gradient

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