Intelligent Surveillance with Real Time Object Detection Coupled with Distance Estimation and Voice Alerts

This research study presents an intelligent surveillance system that seamlessly integrates real-time object detection, precise distance estimation, and voice alerts to significantly enhance monitoring capabilities and situational awareness across various environments. Leveraging state-of-the-art deep learning techniques, particularly the EfficientNetV2 model, the proposed system performs well in accurately identifying a wide range of objects within the camera’s field of view while effectively measuring their distance. Upon detection, the system provides immediate auditory feedback, announcing the object’s name and distance, which not only ensures prompt user responses but also enhances overall safety in dynamic settings. Additionally, the software can send automated MMS notifications that include detailed information about detected objects, such as descriptions, distances, and URL links for real-time updates, facilitating seamless communication. The system is designed for deployment on low-cost, readily available hardware, making it an affordable and efficient solution for diverse applications. Furthermore, it offers customizable audio feedback options, allowing users to select their preferred voice type, language, and volume, thereby enhancing the overall user experience. By integrating these innovative features, this project aims to deliver a versatile, accessible, and robust intelligent surveillance solution that significantly improves monitoring efficiency, responsiveness, and safety, making it an invaluable tool for both security applications and everyday navigation.