RIECNN: Real-Time Image Enhanced CNN for Traffic Sign Recognition
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.
ProofPaper ↗
Key points
- 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.
- Experiments are conducted using the German Traffic Sign Benchmark (GTSRB), the Belgium Traffic Sign Classification (BTSC), and the Croatian Traffic Sign (rMASTIF) benchmark.
- 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.
Sources (1)
- [1]RIECNN: Real-Time Image Enhanced CNN for Traffic Sign RecognitionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 07:01 PM
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.
Traffic sign recognition plays a crucial role in the development of autonomous cars to reduce the accident rate and promote road safety.
Extractive summary: sentences quoted from the sources.