Traffic sign detection plays a vital role in intelligent transportation systems, supporting the safe operation of advanced driver assistance systems and autonomous vehicles. Despite significant progress in real-time object detection, accurately identifying traffic signs under challenging conditions remains difficult. Variations in lighting, adverse weather, motion blur, and partial occlusion often reduce detection performance, especially for small or visually degraded signs where important spatial information can be lost. To address these challenges, we propose Squeeze-Aware-YOLO, a lightweight traffic sign detection framework that introduces a multi-branch bottleneck module combining convolutional kernels of different sizes with a Squeeze-and-Excitation mechanism to improve feature extraction and adaptive channel weighting. In addition, we integrate the Multi-Branch Auxiliary Feature Pyramid Network and Re-parameterized Heterogeneous Multi-Scale modules to strengthen multi-scale feature representation and feature fusion while preserving real-time inference efficiency for practical deployment. The proposed approach is evaluated on the Bangladeshi Traffic Sign Detection Dataset, the CSUST Chinese Traffic Sign Detection Benchmark 2021, and the German Traffic Sign Detection Benchmark, achieving [email protected] scores of 74.7%, 79.4%, and 84.0%, respectively, while consistently outperforming recent state-of-the-art YOLO-based traffic sign detectors. These findings demonstrate that combining multi-scale receptive fields, channel attention, and enhanced feature fusion enables more accurate and robust traffic sign detection while maintaining the computational efficiency required for real-world deployment.
Figures & tables
Fig. 1: Overview of the proposed Squeeze-Aware-YOLO architecture. The model consists of an SE-enhanced input stage with a custom bottleneck block, a hierarchical backbone built using RepHMS modules with multi-scale receptive fields, and a multi-branch neck incorporating Superficial Assisted Fusion (SAF) and Advanced Assisted Fusion (AAF) for effective feature aggregation.
Fig. 2: Simplified view of the SE-enhanced bottleneck block, showing the dual-path structure with sequential bottleneck processing and a compressed bypass path.
Fig. 3: Two bottleneck variants used in the proposed architecture. (a) Bottleneck Type-2 employs a 5×5 convolution, whereas (b) Bottleneck Type-1 uses a 3×3 convolution.
Fig. 4: Detailed SE-enhanced bottleneck block, showing the dual-path structure: the main path processes through three sequential bottlenecks (Type-1, Type-2, Type-1) with varying kernels, while the bypass path provides direct gradient flow.
Fig. 5: Qualitative detection comparison across three traffic-sign datasets. Rows correspond to the BTSD, CCTSDB2021, and GTSDB datasets, and columns show results from YOLOv8, YOLOv10, YOLOv11, MHAF-YOLO, and our method.
Baseline band
Classes
Mean Δ mAP
Improved
Hard ( <0.60 )
4
+0.359
4/4
Moderate ( ≥0.60 , <0.90 )
7
+0.004
3/7
Easy ( ≥0.90 )
4
−0.310
0/4
TABLE V: Per-Class [email protected] Change ( Δ ) on BTSD Relative to the Best of YOLOv8, YOLOv10, and YOLOv11, Grouped by That Baseline’s Difficulty
a TLB denotes the Triple Layer Bottleneck module, and SE denotes the Squeeze-and-Excitation module. Values in the final row correspond to the complete proposed configuration (MHAF + TLB + SE).
TABLE VI: Ablation Study Showing [email protected] for Different Module Configurations on the BTSD, CCTSDB2021, and GTSDB Datasets
Fig. 6: Computational complexity of the evaluated models on CCTSDB2021 and BTSD: (a) GFLOPs for model and (b) parameter count (millions).
Fig. 7: Per-stage runtime decomposition (preprocessing, inference, postprocessing) on (a) CCTSDB2021 and (b) BTSD. Labels above each bar report the total end-to-end latency.
Traffic sign detection is a fundamental component of environmental perception in autonomous driving and intelligent transportation systems. However, most existing detectors rely on static inference with globally shared parameters, limiting their ability to adapt to diverse and unstructured traffic scenarios. As a result, a single static model often struggles to simultaneously handle both clear near-range samples and challenging conditions such as distant small targets or adverse weather environments. To address this limitation, we propose CBDES MoE TSR, a hierarchically decoupled heterogeneous mixture-of-experts(MoE) framework for traffic sign recognition. The proposed framework departs from the conventional globally shared parameter paradigm by introducing a heterogeneous You Only Look Once (YOLO) expert pool together with a lightweight gating network, enabling an image-level dynamic routing mechanism. Based on the semantic characteristics of the input image, the gating module selectively activates the most suitable expert model from the expert pool, enabling a shift from fixed parameter fitting to on-demand dynamic representation. This design enhances feature extraction capability for specific scenarios while maintaining controlled inference overhead. Experimental results demonstrate that the proposed method achieves a remarkable balance between detection accuracy and efficiency on the composite traffic sign dataset. Specifically, our method attains an mAP50-95 of 76.8%, yielding a 2.3% improvement over the baseline method (74.5%) while simultaneously reducing computational overhead by approximately 39.4%. These findings robustly validate the effectiveness of the proposed approach.
Mingxiao Wang, Xiaozhen Qu, Bolin Gao +2
School of Automotive and Traffic Engineering, Liaoning University of Technology, Jinzhou, Liaoning 121001, China. · State Key Laboratory of Intelligent Green Vehicles and Mobility, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.
Traffic sign detection is a challenging visual signal processing task for advanced driver assistance, where small objects, scale variation, and occlusion limit conventional detectors with fixed receptive fields. This paper proposes State-space Modeling and Dynamic Dual Fusion Network (SMDDFNet), a deep learning detector for traffic sign images. SMDDFNet integrates a Dynamic Dual Fusion (DDF) module and a state-space modeling backbone to enhance multi-scale feature representation. DDF combines efficient multi-scale attention with content-aware dynamic filtering in the frequency domain, while the backbone captures long-range dependencies with linear computational complexity. A multi-scale feature fusion neck further aggregates pyramid features for robust localization of small signs. Experiments on TT100K, GTSDB, PASCAL VOC, and the Roboflow~100 vehicle subset show that SMDDFNet achieves competitive accuracy against recent detectors while retaining real-time throughput. The source code is available at https://github.com/rainbowyuyu/SMDDFNet
TianYi Yu, DaJian Zhong, Lilin Wang
Collage of Infomation Engineering, Shanghai Maritime University, Shanghai, 201306, China
Traffic sign recognition plays a crucial role in the development of autonomous cars to reduce the accident rate and promote road safety. It has been a necessity to address traffic signs that are affected significantly by the environment as well as poor real-time performance for deep-learning state-of-the-art algorithms. In this paper, we introduce Real-Time Image Enhanced CNN ({\our}) for Traffic Sign Recognition. {\our} is a real-time, novel approach that tackles multiple, diverse traffic sign datasets, and out-performs the state-of-the-art architectures in terms of recognition rate and execution time. Experiments are conducted using the German Traffic Sign Benchmark (GTSRB), the Belgium Traffic Sign Classification (BTSC), and the Croatian Traffic Sign (rMASTIF) benchmark. Experimental results show that our approach has achieved the highest recognition rate for all Benchmarks, achieving a recognition accuracy of 99.75% for GTSRB, 99.25% for BTSC and 99.55% for rMASTIF. In terms of latency and meeting the real-time constraint, the pre-processing time and inference time together do not exceed 1.3 ms per image. Not only have our proposed approach achieved remarkably high accuracy with real-time performance, but it also demonstrated robustness against traffic sign recognition challenges such as brightness and contrast variations in the environment.
Reem Abdel-Salam, Rana Mostafa, Ahmed H. Abdel-Gawad