AI-Powered Computer Vision Systems for Automated Safety Compliance Monitoring and PPE Detection in Automobile Manufacturing Plants
TL;DR
Results indicate the vision model achieves 92% PPE detection accuracy and practical implications highlight the need for robust encryption user training and continuous model retraining, and future research should examine long-term behavioral impacts and cross-plant scalability.
Abstract
This study investigates the deployment of AI-powered computer vision systems for automated safety compliance monitoring and personal protective equipment (PPE) detection in automobile manufacturing. Four objectives guide the research evaluating detection accuracy measuring incident reporting speed improvements assessing worker acceptance and identifying data security challenges. A mixed-method approach combined with system performance logs with responses from a 100-person survey. Hypothesis tests include one-sample t-tests for accuracy benchmarks paired t-tests for reporting times one-sample t-tests for acceptance scores and chi-square tests for security challenge reports. Results indicate the vision model achieves 92% PPE detection accuracy (t(99)=6.5p<0.001) reduces reporting time by an average of 3.2 minutes (t(99)=7.8p<0.001) yields high acceptance (mean=4.1 t(99)=8.2p<0.001) but reveals moderate security concerns (χ²(1)=12.4p<0.001). Practical implications highlight the need for robust encryption user training and continuous model retraining. Future research should examine long-term behavioral impacts and cross-plant scalability.