Mobile geo-tagging and cloud-based underwater garbage identification using convolutional neural network

Abstract

Water is the essence of life, and water pollution is a major threat to all living things on this planet. To provide solutions to help combat water pollution, we have created a device that would help locate and identify the different garbage types underwater. This paper focused on the detection and identification of cans, plastics, polystyrenes, and glass underwater using object detection and object identification by Convolutional Neural Network and Geotagging. The system set-up comprises the following: a webcam, power bank, Raspberry Pi, GPS module, and an improvise floater. The GUI will display the camera's captured video, the number of garbage identified, and its location in coordinates. The testing was done in two ways: different water visibility and different water levels. The identification accuracy of our program is 94.33% for plastics, 97.34% for glass, 96.89% for polystyrenes, 98.22% for cans, and 96.88% for random garbage, reliability for identification is 100% for plastics, 91.67% for glass, 91.67% for polystyrenes, 95.83% for cans, and 91.67% for random garbage. The mean, median, and mode for the visibility levels are 96.375, 98, and 99, and the depth level is 96.385, 98, and 99.