Weng, Y.; Li, Z.; Chen, X.; He, J.; Liu, F.; Huang, X.; Yang, H. A Railway Track Extraction Method Based on Improved DeepLabV3+. Electronics2023, 12, 3500.
Weng, Y.; Li, Z.; Chen, X.; He, J.; Liu, F.; Huang, X.; Yang, H. A Railway Track Extraction Method Based on Improved DeepLabV3+. Electronics 2023, 12, 3500.
Weng, Y.; Li, Z.; Chen, X.; He, J.; Liu, F.; Huang, X.; Yang, H. A Railway Track Extraction Method Based on Improved DeepLabV3+. Electronics2023, 12, 3500.
Weng, Y.; Li, Z.; Chen, X.; He, J.; Liu, F.; Huang, X.; Yang, H. A Railway Track Extraction Method Based on Improved DeepLabV3+. Electronics 2023, 12, 3500.
Abstract
Extracting railway tracks is crucial for creating electronic railway maps. Traditional methods require significant manual labor and resources, while existing neural networks have limitations in efficiency and precision. To address these challenges, a railway track extraction method using an improved DeepLabV3+ model is proposed, which incorporates several key enhancements. Firstly, the encoder part of the method utilizes the lightweight network MobileNetV3 as the backbone extraction network for DeepLabV3+. Secondly, the decoder part adopts the lightweight universal upsampling operator CARAFE for upsampling. Lastly, to address any potential extraction errors, morphological algorithms are applied to optimize the extraction results. Additionally, a dedicated railway track segmentation dataset is created to train and evaluate the proposed method. The experimental results demonstrate that the model achieves impressive performance on the railway track segmentation dataset and DeepGlobe dataset. The MIoU scores are 88.93% and 84.72%, with Recall values of 89.02% and 86.96%. Moreover, the overall accuracy stands at 97.69% and 94.84%. The algorithm's operation time is about 5% lower in comparison to the original network. Furthermore, the morphological algorithm effectively eliminates errors like holes and spots. These findings indicate the model's accuracy, efficiency, and the enhancement brought by the morphological algorithm in error elimination.
Keywords
Deep Learning; MobileNetV3; Morphological algorithm; Railway extraction; Aerial Imagery
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
