An improved single short detection method for smart vision-based water garbage cleaning robot

Abstract

These days, plastic trash is exponentially overwhelming our waterways. The catastrophe has attracted global attention at this point. As a result, protecting the environment on the water's surface has received increasing focus. Currently, manpower can be used to clean up contaminated water bodies like ponds, rivers, and oceans. Using the current cleaning approach results in low efficiency and hazard. The detection, collection, sorting, and removal of plastic trash from such water surfaces has been the subject of relatively little robotic research, despite the dire circumstances. From private sources, there are very few individual efforts to be found. In order to attain great efficiency without human assistance or operation, a fully autonomous water surface cleaning robot is proposed in this study. The robot was created to adapt to any type of water body found in the real world. An efficient object identification machine learning technique can be suggested for the creation of autonomous cleaning robots. This study improved the Single Short Detection (SSD) method to recognise objects accurately. Because of the enhanced detection techniques, the robot is able to collect trash on its own. With a mean average precision (mAP) of 94.099 % and a detection speed of up to 64.67 frames per second, experimental findings show that the enhanced SSD has exceptional detection speed and accuracy.