Traffic Sign Detection and Recognition with Deep CNN Using Raspberry Pi 4 in Real-time

Automatic traffic sign detection and recognition is crucial and has the potential to be utilized for driver assistance to reduce collisions in driverless cars. A lot of applications in the automotive sector are built around the computer vision challenge of traffic sign detection. The proposed method uses deep Convolutional Neural Networks (CNN) to detect and recognize traffic sign images in real-time. U sing images of the German Traffic Sign Recognition Benchmark (GTSRB) as training data, a sequential CNN model has been built which will be utilized to recognize and classify the unlabeled traffic signs in this challenge. There are 43 classes in the image dataset. The model is implemented in hardware using Raspberry Pi 4 and a web camera.