Computer Vision Based Obstacle Identification Using Real-Time Illumination Sensor Data

In this study, an IoT based system is developed to monitor obstacles at the indoor surface using mobile sensors in a client-server wireless network. An IR transceiver system gives the positional information and light intensity sensor measures the lux values. An embedded wifi enabled microcontroller is interfaced with the sensors and performs as the client system. The client module is placed over the roof of a car and when it moves through a particular indoor space, it collects the positional illumination data and transmit them to the server unit. The captured sensor values are stored in server laptop as MS-Excel file under the influence of a wifi router. By using offline processing, the real-time sensor data is converted into an image and filtering methods are applied for linear and nonlinear noise removal. Then, edge detection techniques like Canny, Prewitt, Sobel, and Roberts methods are applied to detect the presence of obstacles. The study is repeated for another room to find out the best possible obstacle identification method. Finally, it was concluded that the Canny’s algorithm provides the most accurate identification of static obstacles for both the rooms.