Publication

Abstract : The Intelligent Safety Surveillance System (ISSS) for Industrial Environments is a comprehensive solution designed to enhance workplace safety by monitoring Personal Protective Equipment (PPE) compliance and detecting worker drowsiness. Leveraging computer vision and machine learning technologies, the ISSS provides real time monitoring and proactive alerting mechanisms. This system employs strategically positioned cameras to detect and recognize PPE items such as helmets, safety masks, and gloves. Deep learning algorithms analyze video feeds to identify instances of noncompliance, triggering immediate alerts to workers and supervisors for corrective action. In addition to PPE compliance monitoring, the ISSS utilizes cameras equipped with infrared sensors to monitor workers’ facial expressions and eye movements for signs of drowsiness. Machine learning models are trained to recognize patterns indicative of drowsiness, such as drooping eyelids or prolonged eye closure. Upon detecting these signs, the system alerts workers and supervisors, mitigating the risk of accidents caused by fatigue-related errors. By combining advanced technologies with proactive safety measures, the ISSS aims to create a safer work environment in industrial settings. Its ability to detect non-compliance and drowsiness in real-time enables timely intervention, ultimately reducing the risk of workplace accidents and injuries.