Traffic Sign Detection and Recognition Using Ensemble Object Detection Models

Automated cars are being developed by leading automakers and this technology is expected to revolutionize how people experience transportation, giving people more options and convenience. The implementation of traffic signal detection and recognition systems in automated vehicles can help reduce the number of fatalities due to traffic mishaps, improve road safety and efficiency, decrease traffic congestion, and help reduce air pollution. Once the traffic signs and lights are detected, the driver can then take the necessary actions to ensure a safe journey. We propose a method for traffic sign detection and recognition using ensemble techniques on four models, namely BEiT, Yolo V5, Faster-CNN, and sequential CNN. Current research focuses on traffic sign detection using an individual model like CNN. To further boost the accuracy of object detection, our proposed approach uses a combination of the average, AND, OR, and weighted-fusion strategies to combine the outputs of the different ensembles. The testing in this project utilizes the German Traffic Sign Recognition Benchmark (GTSRB), Belgium Traffic Sign image data, and Road Sign Detection datasets. In comparison with the individual models for object detection, the ensemble of these models was able to increase the model accuracy to 99.54% with validation accuracy of 99.74% and test accuracy of 99.34%.