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@article{174406,
        author = {GOMA E and BARANEEDHARAN S and CHETHAN S N and DINESH REDDY M},
        title = {IOT BASED SPEED CONTROL MONITORING AND ACCIDENT AVOIDANCE USING AI TRAFFIC SIGN DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3668-3673},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174406},
        abstract = {The escalating number of road accidents worldwide, driven by human errors such as driver fatigue, over speeding, and failure to adhere to traffic regulations, underscores the urgent need for advanced safety mechanisms in transportation. This research presents a pioneering system that integrates the Internet of Things (IoT) with artificial intelligence (AI) to mitigate these risks. A Multi-tasking Convolutional Neural Network (MConNN) is engineered to detect and classify traffic signs in real time, while IoT sensors monitor vehicle dynamics and driver behaviour. The system, implemented on an embedded platform, captures traffic sign data via a webcam, processes it using deep learning algorithms, and initiates automated responses like speed modulation or vehicle cessation when hazardous conditions are identified. With high precision in recognizing even small or obscured traffic signs, this approach reduces reliance on human intervention, offering a proactive, cost-effective solution to enhance road safety. Experimental outcomes validate its efficacy, demonstrating its potential as a scalable framework for accident prevention and traffic management in diverse settings.},
        keywords = {IoT, MConNN, Traffic Sign Detection, Road Safety, Deep Learning, Real-Time Monitoring, Vehicle Control},
        month = {March},
        }