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Title	Optimizing Cnn Architecture Using Genetic Algorithm For Classification Of Traffic Signs In Real Time
ID_Doc	40775
Authors	Malhotra R.; Saanidhi; Gupta D.
Year	2023
Published	Lecture Notes in Networks and Systems, 473
DOI	http://dx.doi.org/10.1007/978-981-19-2821-5_46
Abstract	With the notion of smart cities transforming cities into digital societies and making people's lives easier in every way, Intelligent Transportation Systems have become an integral element among all. The Intelligent Transportation System (ITS) attempts to improve traffic efficiency by reducing congestion and ensuring the safety and comfort of commuters in real time. Traffic sign detection and recognition is one of the multifaceted conjunctive fields of research in ITS. In this paper, we address the issue of the TSR (traffic sign recognition) problem, i.e., classification of traffic signs along the roadside which plays a crucial role in developing advanced driver assistance and autonomous driving systems. CNN's network design has a huge impact on its performance and convergence. As a result, we use the Genetic Algorithm (GA) to automate the task of selecting a high-performance CNN (Convolutional Neural Network) Architecture for the GTSRB (German Traffic Sign Recognition Benchmark) dataset. The model is optimized through GA using multiple network configurations in the search space. Our model takes into account the limitations of the dataset, and we use certain data augmentation approaches to address the issues. We were able to attain an average accuracy of 98.2% which demonstrates the state-of-the-art performance on the publicly available dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Convolutional neural network; Deep learning; Genetic algorithm; Model optimization; Multiclass classification

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