DFA-UNet: Efficient Railroad Image Segmentation
Source abstract
Abstract
In computer vision technology, image segmentation is a significant technological advancement for the current problems of high-speed railroad image scene changes, low segmentation accuracy, and serious information loss. We propose a segmentation algorithm, DFA-UNet, based on an improved U-Net network architecture. The model uses the same encoder–decoder structure as U-Net. To be able to extract image features efficiently and further integrate the weights of each channel feature, we propose to embed the DFA attention module in the encoder part of the model for the adaptive adjustment of feature map weights. We evaluated the performance of the model on the RailSem19 dataset. The results showed that our model showed improvements of 2.48%, 0.22%, 3.31%, 0.97%, and 2.2% in mIoU, F1-score, Accuracy, Precision, and Recall, respectively, compared with U-Net. The model can effectively achieve the segmentation of railroad images.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Infrastructure Maintenance and Monitoring · Vehicle License Plate Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
Institute of Automation, Shandong Academy of Sciences, Jinan 250013, China
Funding
Natural Science Foundation of Shandong Province · China Postdoctoral Science Foundation
References (17)
Show source-provided references
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