Forthcoming Articles

International Journal of Signal and Imaging Systems Engineering

International Journal of Signal and Imaging Systems Engineering (IJSISE)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are also listed here. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

International Journal of Signal and Imaging Systems Engineering (One paper in press)

Regular Issues

  • Real-Time Traffic Scene Segmentation with SegFormer-B0   Order a copy of this article
    by Rishit Varma Manthena, Suganeshwari G 
    Abstract: Autonomous Vehicles must ensure accurate process scene information to navigate safely, yet many segmentation procedures struggle with moving scenes, occlusions, or environmental factors. This work employs the lightweight Transformer-based SegFormer-B0 model for the semantic segmentation of freeway scenes. The architecture utilises overlapping patch embeddings, efficient self-attention, and a lightweight MLP decoder to strike a balance between accuracy and computational cost, making it well-suited for real-time deployment. The experiments demonstrate that SegFormer-B0 performs well, achieving a mean IoU (mIoU) of 0.74, an F1 score of 0.80, and a pixel accuracy of over 83%. A comparative analysis against baseline methods, such as U-Net, DeepLabv3+, and ResNet, reveals that SegFormer achieves a better balance between segmentation accuracy, inference time, and energy efficiency. Qualitative assessments show consistent segmentation of vehicles, pedestrians, lane markings, and traffic signs during harsh conditions. These results highlight that SegFormer-B0 presents a promising component for enhancing autopilot perception pipelines.
    Keywords: Computer Vision; Image Processing; Transformer Models; Semantic Segmentation; Autonomous Driving.
    DOI: 10.1504/IJSISE.2026.10079237
     

 

Return to top