Customized Traffic Sign Detection

In With a growing emphasis on road safety, there's an increasing demand for dependable traffic sign detection systems. This study introduces an innovative approach that employs a customized Convolutional Neural Network (CNN) architecture to tackle the challenges faced by conventional methods. These challenges include the necessity for accurate detection under varying lighting conditions, complex backgrounds, and diverse sign dimensions. Our tailored CNN model integrates specialized layers and optimizations, such as feature extraction, spatial hierarchies, and context-aware modules, aimed at improving precision and recall in traffic sign recognition. Feature extraction layers utilize a blend of convolutional and residual blocks to capture intricate patterns and contextual information. Spatial hierarchies are established through carefully designed pooling and up sampling layers, enabling the model to grasp spatial relationships within sign images. Moreover, context-aware modules refine the detection process by integrating global contextual information, ensuring accurate classification even in complex scenarios. Evaluation conducted on a comprehensive dataset demonstrates significant improvements in detection accuracy compared to traditional CNN architectures. Additionally, the proposed model exhibits robust performance across diverse environmental conditions, making it suitable for real-world applications and contributing to the advancement of intelligent transportation systems, thereby promoting safer roads.