Research and Design of Marine Trash Classification Robot Based on Color Recognition

Abstract

The UN (United Nations) identifies sustainable development of the marine ecosystem as a major goal. To achieve the goal, the garbage in the ocean has to be removed, and as people recognize that different types of classification should be disposed differently, classification is critical after garbage is collected. This led to the engineering goal of constructing a robot that can pick up garbage in the water and classify the garbage into its right category. The robot was developed by attaining four critical functions: driving under water condition, a set of gripper, color recognition and garbage classification. Results of data obtained from developing the garbage classification was plotted on multiple graphs including training and validation loss, training time, training and testing accuracy and so on. After the critical functions are fulfilled, it is shown that the accumulative testing accuracy for garbage classification algorithm was around 90.6%, while the programs for the other three critical functions all compiled successfully. It was a regret that datasets weren’t shaped to the same sizes and the critical functions should be synthesized for further research.

A Survey on Waste Detection and Classification Using Deep Learning

“…In [79], the authors focus on the classification of garbage using metadata and evaluated the strategy using multiple deep learning algorithms such as VGG16, ResNet50, and DenseNet169 to compare it with the recently developed model ThanosNet, which achieved an accuracy of 94%. A lot of more research focuses on trash image classification from different devices such as in [80] for robotics, and those purely works with CNN with low accuracy such as in [81] and [82] using different benchmark datasets. The summary of the trash classification-based research is in Table 3:…”