AIoT-Based Rail Fastener Assessment in Low-Light Environment Using Focal Modulated Unsupervised Image Enhancement and Generative Segmentation

Ensuring the integrity of railway fasteners is essential for maintaining track stability and operational safety, particularly under high-speed and dynamic loading conditions. However, reliable inspection becomes challenging in low-light environments, where conventional sensor-based systems often fail due to high costs, limited scalability, and poor visibility. To address this problem, this study introduces a novel artificial Internet of things (AIoT)-driven framework that enables real-time and low-cost fastener inspection using mobile imaging devices. The proposed method integrates zero-reference deep curve estimation enhanced with focal modulation and squeeze-excitation attention mechanisms to effectively enhance image visibility in uncertain illumination. The enhanced images are then utilized to train a generative adversarial network (GAN) for precise segmentation and defect detection of fasteners. Unlike traditional enhancement and segmentation pipelines, this work uniquely fuses illumination correction, attention-guided modulation, and generative learning into a unified end-to-end AIoT framework. This integration not only addresses the persistent problem of low-light degradation but also introduces a scalable and adaptive solution suitable for on-site railway monitoring. Experimental evaluation demonstrates that the proposed enhancement network achieves superior visual restoration with a minimized total loss of 1.1823 and optimal curve configuration, while the GAN attains high segmentation precision and robustness when trained on augmented datasets, confirming its effectiveness under real-world lighting variations. The validated framework demonstrates strong potential for scalable, cost-efficient, and accurate fastener monitoring, contributing to intelligent and predictive maintenance in modern railway infrastructure.

• Record URL:
  • https://doi.org/10.1061/JTEPBS.TEENG-9494
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/24732907
• Supplemental Notes:
  • © 2026 American Society of Civil Engineers.
• Authors:
  • Qiu, Shi
  • Shah, S Muhammad Ahmed Hassan
  • Wang, Weidong
  • Yamin, Seemab
  • Wang, Jin
  • Ai, Chengbo
  • Zaheer, Qasim
  • 0000-0002-8158-6837
• Publication Date: 2026-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 04026041
• Serial:
  • Journal of Transportation Engineering, Part A: Systems
  • Volume: 152
  • Issue Number: 7
  • Publisher: American Society of Civil Engineers
  • ISSN: 2473-2907
  • EISSN: 2473-2893
  • Serial URL: http://ascelibrary.org/journal/jtepbs

Subject/Index Terms

• TRT Terms: Data segmentation; Image processing; Inspection; Internet of things; Maintenance of way; Rail fasteners
• Subject Areas: Data and Information Technology; Maintenance and Preservation; Railroads; Vehicles and Equipment;

Filing Info

• Accession Number: 01998395
• Record Type: Publication
• Files: TRIS, ASCE
• Created Date: Aug 5 2026 9:14AM