Smart Noise Violation Detection and Automated E-Challan System Using AI-Based Number Plate Recognition

Urbanization and the rapid growth of vehicular transportation have significantly increased environmental noise pollution in modern cities. Traffic noise generated by excessive honking, modified exhaust systems, and aggressive driving behavior has become a major concern for urban authorities and residents. Continuous exposure to high noise levels can negatively impact human health, causing stress, sleep disturbance, and hearing impairment. Traditional monitoring approaches depend heavily on manual inspection by traffic authorities, which limits the effectiveness of enforcement due to manpower constraints and the absence of real-time monitoring mechanisms. Therefore, the development of automated and intelligent monitoring systems has become essential to control vehicular noise pollution in smart cities. Recent studies have explored sensor-based and intelligent monitoring techniques for traffic noise detection and environmental monitoring. Intelligent acoustic sensor systems have been developed to analyze vehicle-generated sound and estimate traffic composition using sound wave analysis and computer vision techniques [1]. Similarly, automated traffic monitoring systems integrating sensors and embedded platforms have demonstrated the capability to detect excessive vehicle noise and trigger cameras to capture vehicle information for further enforcement [2]. Research in machine learning and computer vision has also enabled the use of Automatic Number Plate Recognition (ANPR) systems to identify vehicles involved in traffic violations such as overspeeding or pollution violations [3]. Additionally, advanced audio analysis models have been proposed to classify vehicular honks and identify the source of noise using deep learning-based sound recognition techniques [4].