raffic sign detection is the process of identifying and localizing traffic signs in an image or video. This technology is essential for self-driving cars and advanced driver assistance systems (ADAS) as it helps in detecting traffic signs such as speed limits, stop signs, and traffic signals.
The process of traffic sign detection involves several steps such as image preprocessing, feature extraction, classification, and post-processing. In the image preprocessing stage, the image is resized and enhanced to improve its quality. In the feature extraction stage, relevant features of the traffic sign are extracted, such as its color, shape, and texture.
In the classification stage, a machine learning algorithm is used to classify the traffic sign based on the extracted features. Deep learning techniques such as convolutional neural networks (CNNs) have shown promising results in traffic sign detection.
In the post-processing stage, the detected traffic sign is refined and localized accurately. This is achieved through techniques such as non-maximum suppression (NMS) and object tracking.
Overall, traffic sign detection is a critical component of autonomous driving technology and plays an important role in ensuring the safety of both drivers and pedestrians on the road.
In this project we used GTSRB dataset. The German Traffic Sign Recognition Benchmark (GTSRB) dataset is a widely used dataset for evaluating and benchmarking traffic sign detection and recognition algorithms. The dataset contains more than 50,000 images of traffic signs, which are divided into 43 classes based on their shape and meaning.
The images were captured under various conditions, such as different weather conditions, illumination, and viewing angles. This makes the dataset more challenging and realistic, and helps in evaluating the robustness of traffic sign detection algorithms.
The GTSRB dataset has been widely used for developing and evaluating traffic sign detection and recognition algorithms, and has led to significant improvements in the field. It is commonly used as a benchmark dataset for research papers, and many state-of-the-art algorithms have been evaluated on this dataset.
The dataset is freely available for download, and it can be used for both commercial and non-commercial purposes. The GTSRB dataset is a valuable resource for researchers and developers working on traffic sign detection and recognition algorithms, and it has contributed significantly to the advancement of autonomous driving technology.