Manhole cover monitoring system over IOT

Manhole recognition and monitoring is one of the essential needs for modern society, particularly smart city plan. The idea of this project roots in fact that missing or stolen manholes results in various road accidents and it shrinks the quality of city. The major challenge in this research is to investigate a method in recognition of manholes and further investigation on the condition of manhole on road. The report can be simultaneously updated in IOT platform which can be further used with other applications such as WAZE or Google MAP. This method uses Naïve Bayes classifier to train dataset and detect as well as recognize the missing manhole on road. This project has three main components including 1) Recognize manhole on road, 2) Detect missing manhole, and 3) Update the coordinates to IOT platform. The software used for image processing was MATLAB. The coordinates were obtained via Arduino Mega and SIM808 GSM modem to updates the missing manhole covers coordinates on BLYNK IOT. The result obtained from the accuracy of the Naïve Bayes classifier indicates that the system can perfectly detect the missing manhole with total accuracy of 85%. The True Negative Rate (TNR), Positive Predictive Value (PPV) and False Discovery Rate (FDR) were found to be 80%, 90%, and 10%, respectively. The data shows that the system results in 100% accuracy in detection for speed of below 20 KMPH. The result indicates that the camera can varied from 30 to 42 degrees while obtaining best result without any misdetection. The rate of recognition for the system with 30 fps is found to be 71.58% by dividing the total time for processing a frame over the total frame time duration). The Latency test in obtaining the coordinates indicates that the proposed system takes 4.724 seconds in average to obtain the coordinates from GSM modem.