Deep Learning for Large-Scale Traffic-Sign Detection and Recognition (1904.00649v1)

Abstract: Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community the recognition and detection of traffic signs is a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced drivers-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs (around 50 categories out of several hundred) and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the Mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 200 traffic-sign categories represented in our novel dataset. Results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of traffic-sign inventory management.

• The paper introduces Mask R-CNN adaptations, including online hard-example mining, to address high intra-class variations in traffic-sign detection.
• It pioneers a data augmentation strategy that simulates real-world geometric and appearance variations, enhancing recognition of small and occluded signs.
• The research establishes a novel 200-category DFG dataset, achieving competitive precision with error rates below 3% in practical detection scenarios.

Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

This paper presents a comprehensive approach to automating the detection and recognition of a large variety of traffic signs using advanced deep learning techniques. Traffic-sign inventory management plays a critical role in maintaining road safety, necessitating a system that can efficiently identify numerous types of road signs with minimal human intervention. The authors propose using the Mask R-CNN model as the basis for their system, a state-of-the-art approach in object detection networks, renowned for its accuracy and speed. In this research, Domen Tabernik and Danijel Skočaj make specific adaptations to improve its application to traffic-sign detection, and also introduce a novel dataset - the DFG traffic-sign dataset.

Key Contributions

1. Mask R-CNN Adaptations:
   • The authors modify the standard Mask R-CNN to cater to the unique challenges posed by traffic-sign detection, which involves high intra-category appearance variations and low inter-category variance. They implement online hard-example mining (OHEM) to focus the learning process on the toughest instances, and modify the training sample distribution and weighting to ensure balanced learning from diverse object sizes.
2. Data Augmentation Techniques:
   • Given the diversity and complexity of traffic signs globally, the authors develop a data augmentation strategy to simulate real-world variations and enrich the dataset. This strategy leverages the variation in geometric and appearance aspects derived from extensive real-world traffic sign observations.
3. The DFG Dataset:
   • They introduce a comprehensive dataset specifically developed for benchmarking traffic-sign detection methods, consisting of 200 categories with over 13,000 instances. This dataset encompasses traffic signs from various environments to enable robust training and evaluation.

Experimental Insights

The experiments demonstrate the Mask R-CNN’s capability, with proposed adaptations significantly enhancing performance on smaller and more varied traffic signs. They evaluated the system against previous state-of-the-art methods on established datasets like the Swedish Traffic-Sign Dataset (STSD), achieving competitive or superior results in terms of precision, recall, and mAP metrics. Their adaptations show a clear improvement in both region proposal effectiveness and end-to-end detection performance, reducing error rates particularly in challenging scenarios involving small and heavily occluded traffic signs.

Theoretical and Practical Implications

The methodological advancements in this paper have substantial practical implications for traffic infrastructure management and autonomous driving systems. With a reported error rate below 3%, the framework provides a reliable and efficient foundation for real-time traffic-sign inventory management. From a theoretical perspective, the integration of OHEM within Mask R-CNN and the novel augmentation approaches imply potential pathways for extending object detection models to other domains characterized by similar challenges of high intra-class variability.

Future Directions

The research underlines several possible directions for future work. One promising avenue is refining the classification network to minimize missed detections further. Moreover, expanding the dataset to encompass even more categories and regional variations could enhance the system's robustness and adaptability. The discussion also hints at exploring deeper neural architectures or hybrid approaches that might combine the strengths of Mask R-CNN with other emerging techniques for enhanced performance across different object detection tasks.

In summary, this paper presents significant progress in leveraging deep learning for robust, large-scale traffic-sign detection and recognition, providing a strong foundation upon which future advancements in automated traffic infrastructure management systems can be built.