Deep Learning Benchmark for Traffic Sign Recognition: Cross-Dataset Evaluation and Model Interpretability Study

This project is a comprehensive traffic sign recognition benchmark framework, released by abhinz16 on GitHub on June 9, 2026 (link: https://github.com/abhinz16/traffic-sign-recognition-benchmark). Its core goal is to integrate multi-dataset training, model robustness evaluation, and Grad-CAM interpretability analysis, providing performance evaluation standards close to real-world scenarios for autonomous driving vision systems.

Traffic sign recognition is a core visual task for autonomous driving and driver assistance systems. However, models trained on a single dataset often suffer from insufficient generalization ability and struggle to handle real-world challenges such as lighting changes, occlusions, and angle variations. This project aims to build a more comprehensive benchmark system to address the limitations of traditional evaluations that only focus on classification accuracy while ignoring robustness and interpretability.

Multi-Dataset Fusion

Integrates datasets such as Germany's GTSRB, Belgium's BelgiumTS, and Mapillary, unifying them into the 43-class GTSRB label space to avoid model overfitting to a single dataset.

Model Support

Built-in mainstream architectures including ResNet18 (balance between efficiency and accuracy), EfficientNet-B0 (compound scaling optimization), and custom lightweight CNNs.

Robustness Evaluation

Simulates real-world interferences such as noise, blurriness, and brightness changes to test model performance in degraded scenarios.

Interpretability Analysis

Generates heatmaps via Grad-CAM to show the image regions the model focuses on during decision-making, verifying whether the model learns semantic features rather than irrelevant backgrounds.

Automated Experimental Workflow

1. Automatic dataset download and preprocessing (normalization, augmentation)
2. Supports single/multi-dataset training modes
3. Calculates metrics such as accuracy and F1 score
4. Generates training curves, confusion matrices, and Grad-CAM examples
5. Outputs classification reports and robustness test results

Technology Stack

Built on Ubuntu 24.04, Python 3.12, PyTorch 2.x, and CUDA 12.x, using mixed-precision training and multi-core data loading to improve efficiency.

• Autonomous Driving R&D: Provides standardized evaluation tools to help teams predict the real-road performance of models and narrow the gap between lab and deployment results.
• Academic Research: Open-source evaluation protocols and visualization tools lay the foundation for fair comparison of different methods in the field and promote technological progress.

The project plans to introduce support for the Vision Transformer architecture, ONNX model export, real-time inference benchmarking, and domain adaptation methods in the future to further enhance the practicality of industrial-grade applications.

This benchmark builds an evaluation system close to real-world scenarios through three pillars: multi-dataset fusion, robustness evaluation, and interpretability analysis. Core insight: In deep learning model development, accuracy is only the starting point; understanding the model's behavior boundaries and decision-making mechanisms is the key to building reliable systems.