Abstract
Abstract: Helmet detection and license plate recognition have emerged as crucial components in ensuring safety and security in various domains, particularly in transportation and law enforcement. This review paper presents a comprehensive survey and analysis of methodologies, techniques, and advancements in the field of helmet detection and license plate recognition using computer vision and deep learning approaches. The paper investigates diverse datasets, algorithms, and architectures utilized for accurate detection and recognition of helmets worn by individuals in critical settings and the identification of license plates on vehicles. Furthermore, it explores the challenges, limitations, and future directions within these domains. The paper also evaluates the performance metrics, technological advancements, and potential applications in real-world scenarios, emphasizing the importance of these technologies in enhancing safety measures, traffic regulation, and security enforcement systems. Through this review, we aim to provide a consolidated overview and critical analysis of existing methodologies while identifying opportunities for future research and development in helmet detection and license plate recognition.
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