IOT BASED MANHOLE DETECTION AND MONITORING SYSTEM

Abstract

Urban areas frequently face safety risks due to open manholes and increasing environmental pollution. This project proposes an IoT-based Manhole Detection and Monitoring System to enhance urban safety and environmental management. The system uses a raspberry pi camera module integrated with a Raspberry Pi 4 model B to detect manholes through a deep learning model based on YOLO (You Only Look Once). The trained CNN model identifies open and damaged manholes in real time and ensures immediate detection. In addition, pollution levels in specific areas are monitored using environmental sensors. If excessive pollution is detected, the CNN model analyzes the data, predicts the severity level, and automatically generates a report. When abnormal conditions such as open manholes or high pollution levels are identified, the system sends instant text message alerts to the authorities through Email. This enables quick action, reduces accident risks, and helps control environmental hazards. The proposed system provides a smart, automated, and cost-effective solution for urban area monitoring and smart city development.