Toward Efficient Traffic-Sign Detection via SlimNeck and Coordinate-Attention Fusion in YOLO-SMM

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 with a corner-distance term, supplying stable gradients for sub-20-pixel boxes and tightening localization. An evaluation of YOLO-SMM on the German Traffic Sign Recognition Benchmark (GTSRB) revealed that it attained 96.3% mAP50 and 93.1% mAP50-90 at a rate of 90.6 frames per second (FPS). This represents an improvement of +1.0% over previous performance benchmarks. The mAP at 64 × 64 resolution was found to be 50% of the maximum attainable value, with an FPS of +8.3 when compared to YOLOv8. This result indicates superior performance in terms of accuracy and speed compared to YOLOv7, YOLOv5, RetinaNet, EfficientDet, and Faster R-CNN, all of which were operated under equivalent conditions.