| Issue |
E3S Web Conf.
Volume 522, 2024
2023 9th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2023)
|
|
|---|---|---|
| Article Number | 01013 | |
| Number of page(s) | 7 | |
| DOI | https://doi.org/10.1051/e3sconf/202452201013 | |
| Published online | 07 May 2024 | |
Based on the improved installation gap identification algorithm of the DeepLabV3+ spacer rod replacement robot
1 Hunan electric power company ultra high voltage transmission company, 410100 Changsha, China
2 Changsha University of Science and Technology, 410114 Changsha, China
* Corresponding author: 1622431439@qq.com
This paper proposes an improved DeepLabV3+ lightweight algorithm for the identification of installation gaps in spacer replacement robots. By using lightweight MobileNetV3 to extract semantic features of spacer installation gaps, parameters and computational complexity are reduced; Perform dimensionality reduction and dimensionality increase operations on the ASPP module to reduce the number of model parameters; Introduce ECA module to restore the clarity of target boundaries; Use a loss function combining Focal Loss and Dice Loss to enhance segmentation performance. The experimental results show that the improved DeepLabV3+ algorithm improves MIoU, MPA, and prediction speed, while balancing segmentation accuracy and speed, and can effectively segment the installation gap of the spacer.
Key words: Spacer rod replacement robot / Installation clearance / Improved DeepLabV3+ / Lightweight / Attention mechanism
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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