Traffic Sign Board Detection Using CNN

Computer Science Engineering

Traffic sign board detection is an important task in the field of computer vision, with numerous practical applications in the area of autonomous driving, traffic analysis, and intelligent transportation systems. Convolutional neural networks (CNNs) have proven to be highly effective in detecting traffic signs due to their ability to learn hierarchical representations of features from input images. We propose a CNN-based approach for traffic sign board detection. We train our model on a large dataset of annotated traffic sign images, and employ a multi-scale sliding window approach to detect traffic signs of various sizes. Our model incorporates both local and global features to improve detection accuracy, and is able to detect traffic signs under a wide range of lighting and weather conditions. Our experimental results demonstrate that our CNN-based approach achieves state-of-the-art performance in traffic sign board detection, with a high detection rate and low false positive rate.

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