Autotrinet YOLO triple attention framework for robust traffic sign detection

Traffic sign detection (TSD) remains a critical challenge in intelligent transportation systems due to factors such as small target sizes, environmental variability, and real-time constraints. While deep learning-based methods like YOLO have advanced TSD performance, existing approaches often struggle to balance accuracy, computational efficiency, and robustness across diverse scenarios. This paper proposes AutoTriNet-YOLO, a novel framework that integrates triple-attention enhancement (local, global, and sequential pathways) with dynamic feature fusion and adaptive computation to address these limitations. The core innovation lies in the TriplePathBlock module, which parallelizes Convolutional Block Attention (CBAM), Non-local Blocks, and a Lite Transformer to capture multi-scale contextual dependencies efficiently. A Dynamic Fusion Gate adaptively weights attention paths, while a Selective Insert mechanism prunes redundant operations based on input complexity. Evaluated on a comprehensive traffic sign dataset, AutoTriNet-YOLO achieves state-of-the-art performance with 86.6% mAP@50 and 65.3% mAP@50-95, outperforming existing methods like TSD-YOLO and EDN-YOLO. Ablation studies validate the contributions of each component, particularly the CBAM pathway for local feature refinement. The framework maintains real-time efficiency, making it suitable for edge deployment in autonomous driving systems. This work advances robust TSD by unifying diverse attention mechanisms into a scalable, computationally optimized architecture.