Attention to detail: A conditional multi-head transformer for traffic sign recognition
Isra Naz, Jamal Hussain Shah, Ali Tahir, Mahatma Reddy Marri, Rabia Saleem, Mutaz Elradi S. Saeed, Mudassir Khan, Mudassir Khan, Mudassir Khan, Mudassir Khan
TL;DR
This paper introduces a new Vision Transformer model that dynamically adapts to improve traffic sign recognition in autonomous vehicles.
Contribution
The novel Conditional Visual Transformer (CViT) dynamically adjusts attention mechanisms based on input sign type.
Findings
CViT achieved 99.87% overall accuracy in traffic sign recognition.
The model showed high performance with a Macro F1 Score of 99.07%.
Adaptive gating improved projection matrix adjustments across different signs.
Abstract
The challenge of traffic sign detection and recognition for driving vehicles has become more critical with recent advances in autonomous and assisted driving technologies. Although object recognition problems, particularly traffic sign recognition, have been extensively studied, most Vision Transformer (ViT) models still rely on static attention mechanisms with fixed projection matrices (Q, K, and V). Using this mechanism limits the ViTs to handle real-world problems such as object detection and traffic sign recognition, etc. Problems, such as partially or fully obscured signs, changes in illumination, and weather conditions, result in subpar feature extraction, which compounds the misclassification problem. To overcome this challenge, a Conditional Visual Transformer (CViT) is proposed in this research, which dynamically adapts feature aggregation, Q, K, and V projections, as well as…
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Taxonomy
TopicsAdvanced Neural Network Applications · Hand Gesture Recognition Systems · Handwritten Text Recognition Techniques