Abstract
The development of reliable and sustainable traffic sign detection under difficult weather conditions, or DFR-TSD, is a key step in creating effective, safe, and sustainable autonomous driving systems. The suggested sustainable framework makes use of deep learning techniques to overcome the drawbacks of the current traffic sign detection systems, especially in difficult weather circumstances like haze and snow. The system uses a sustainable CNN pre-processing step to make traffic signs more visible in photos that have been impacted by the weather, followed by a sustainable pre-trained ResNet-50 model to recognize traffic signs. On the CURE-TSD dataset, which includes difficult weather circumstances such as haze, snow, and fog, the suggested sustainable framework was assessed. The testing findings showed how sustainably well the suggested framework performed in identifying traffic signs in adverse weather. The suggested sustainable framework outperforms previous approaches with a sustainable accuracy rating of 98.83%. The outcomes show that sustainable deep learning methods have the potential to enhance traffic sign identification models' functionality. The proposed sustainable framework’s front end offers a user-friendly interface that enables users to upload test photographs and view the results of the detection. There are four sustainable buttons on the UI for loading the model, uploading test photographs, spotting signs, and seeing the training graph. The Tkinter framework, which offers a user-friendly GUI toolkit that enables developers to quickly design and customize sustainable GUI programs, is used to develop the front end. The suggested sustainable DFR-TSD framework is a potential sustainable option for reliable traffic sign detection in adverse weather due to the sustainable pre-processing step, the sustainable pre-trained ResNet-50 model, and the sustainable user-friendly interface.
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