Sign-Aware Attention-Based Multitasking Framework for Integrated Traffic Sign Detection and Retroreflectivity Estimation
Published in Expert Systems with Applications, 2025
We propose SAAM-ReflectNet, a deep learning framework that automates traffic sign detection, classification, and retroreflectivity estimation by integrating robust spatial-semantic feature extraction, Sign-Aware Attention, and multimodal fusion of RGB and LiDAR data. Achieving a mean Average Precision (mAP) of 0.635 and RMSEs of 0.169 (foreground) and 0.147 (background), ReflectNet demonstrates scalability and accuracy, making it ideal for large-scale, proactive traffic sign maintenance.
Recommended citation: Asamoah, J. K., Kyem, B. A., Obeng-Amoako, N. D., & Aboah, A. (2025). "Sign-Aware Attention-Based Multitasking Framework for Integrated Traffic Sign Detection and Retroreflectivity Estimation." Expert Systems with Applications, 286, 128003. https://doi.org/10.1016/j.eswa.2025.128003