Hui Chen1, Mohammed A. H. Ali1,*, Bushroa Abd Razak1, Zhenya Wang2, Yusoff Nukman1, Shikai Zhang1, Zhiwei Huang1, Ligang Yao3, Mohammad Alkhedher4
CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-26, 2026, DOI:10.32604/cmc.2025.067286
- 09 December 2025
Abstract Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. However, existing one-stage detectors miss distant signs, and two-stage pipelines are impractical for embedded deployment. To address this issue, we present YOLO-SMM, a lightweight two-stage framework. This framework is designed to augment the YOLOv8 baseline with three targeted modules. (1) SlimNeck replaces PAN/FPN with a CSP-OSA/GSConv fusion block, reducing parameters and FLOPs without compromising multi-scale detail. (2) The MCA model introduces row- and column-aware weights to selectively amplify small sign regions in cluttered scenes. (3) MPDIoU augments CIoU loss… More >
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