Efficient Vision Transformer for Accurate Traffic Sign Detection
Javad Mirzapour Kaleybar Hooman Khaloo Avaz Naghipour
This research paper addresses the challenges associated with traffic sign
detection in self-driving vehicles and driver assistance systems. The
development of reliable and highly accurate algorithms is crucial for the
widespread adoption of traffic sign recognition and detection (TSRD) in diverse
real-life scenarios. However, this task is complicated by suboptimal traffic
images affected by factors such as camera movement, adverse weather conditions,
and inadequate lighting. This study specifically focuses on traffic sign
detection methods and introduces the application of the Transformer model,
particularly the Vision Transformer variants, to tackle this task. The
Transformer's attention mechanism, originally designed for natural language
processing, offers improved parallel efficiency. Vision Transformers have
demonstrated success in various domains, including autonomous driving, object
detection, healthcare, and defense-related applications. To enhance the
efficiency of the Transformer model, the research proposes a novel strategy
that integrates a locality inductive bias and a transformer module. This
includes the introduction of the Efficient Convolution Block and the Local
Transformer Block, which effectively capture short-term and long-term
dependency information, thereby improving both detection speed and accuracy.
Experimental evaluations demonstrate the significant advancements achieved by
this approach, particularly when applied to the GTSDB dataset.