This research introduces an innovative security enhancement approach, employing advanced image analysis and soft computing. The focus is on an intelligent surveillance system that detects unauthorized individuals in restricted areas by analyzing attire. Traditional security measures face challenges in monitoring unauthorized access. Leveraging YOLOv8, an advanced object detection algorithm, our system identifies authorized personnel based on their attire in CCTV footage. The methodology involves training the YOLOv8 model on a comprehensive dataset of uniform patterns, ensuring precise recognition in specific regions. Soft computing techniques enhance adaptability to dynamic environments and varying lighting conditions. This research contributes to image analysis and soft computing, providing a sophisticated security solution. Emphasizing uniform-based anomaly detection, it establishes a foundation for robust security systems in restricted areas. The outcomes highlight the potential of YOLOv8-based surveillance in ensuring safety in sensitive locations.

翻译：本研究提出一种创新的安全增强方法，采用先进的图像分析与软计算技术。重点在于开发一种智能监控系统，通过分析着装检测受限区域内的未授权人员。传统安防措施在监控未授权访问方面面临挑战。利用先进的物体检测算法YOLOv8，本系统能够在闭路电视录像中基于着装识别授权人员。该方法涉及在全面的制服图案数据集上训练YOLOv8模型，确保在特定区域的精确识别。软计算技术增强了系统对动态环境和多变光照条件的适应性。本研究为图像分析与软计算领域作出贡献，提供了一种精密的安全解决方案。通过强调基于制服的异常检测，为受限区域的鲁棒安全系统奠定了基础。研究结果凸显了基于YOLOv8的监控系统在保障敏感区域安全方面的潜力。