A deep-learning-based approach for cable duct recognition

Jian Feng; Jiazheng Li

doi:10.1117/12.3024589

13 May 2024 A deep-learning-based approach for cable duct recognition

Jian Feng, Jiazheng Li

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131595X (2024) https://doi.org/10.1117/12.3024589
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

As the scale of cable installation continues to expand, the arrangement of cables in manholes has become increasingly complex, making manual surveys more challenging. In response to this issue, this study adopts the YOLOX algorithm as the framework, using the lightweight Swin Transformer network as its backbone. In the recognition model, a convolution attention module is integrated, and the original localization loss function is replaced with the Efficient Intersection over Union (EIoU) loss function. Based on the principles mentioned above, the target recognition algorithm was tested. The test results indicate that the improved YOLOX tracking algorithm achieved an accuracy of 86.64%, with a 2.04% increase in precision, a 2.3% increase in recall, and a 2.14% increase in average detection accuracy. This effectively enables the recognition of cable duct openings.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jian Feng and Jiazheng Li "A deep-learning-based approach for cable duct recognition", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131595X (13 May 2024); https://doi.org/10.1117/12.3024589

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