Method and System for Detecting and Recognizing Floating Garbage Moving Targets on Water Surface with Big Data Based on Blockchain Technology

Abstract

At present, the methods for cleaning up floating objects in small water areas such as campus artificial lakes and community sightseeing artificial lakes are mainly to use traditional tools such as hand-held nets to remove them. There are disadvantages such as unclean cleaning, long time-consuming, low efficiency, and high labor intensity. The habit and tradition of allowing the ocean to accept human waste have made almost all sea areas on the planet now full of garbage, from the poles to the equator, from coastal bays to submarine riverbeds. The accumulation of marine litter, especially plastic litter, is considered to be an urgent global environmental problem along with many important issues of our time. In this paper, three typical working environments with good vision, more reflections in the water, and strong light interference are performed to identify floating objects. Each working environment has 100 images to be tested, a total of 300 images, and read the next image after processing one image. The detection rates under the three working environments were 93%, 96%, and 74%, and the average detection rate was 84.3%.