THE APPLICATION OF DRONE-ASSISTED DEEP LEARNING TECHNOLOGY IN RIVERBANK GARBAGE DETECTION

Abstract

 In recent years, the dumping of garbage in rivers has become a common occurrence and has gradually started to affect the normal flow of river channels, which has added lots of work to the river patrol staff. Facing these problems, river authorities urgently need a reasonable and better cost-performance method, that can be adopted on a large scale to support the staff in investigating the garbage within the rivers. Although object detection using Artificial Intelligence has its advantages, it has not been widely applied in the riverine environment using drone. This study attempts to detect garbage in the Asahi River, Japan using two object detection models. By using a large amount of PET images collected from the Internet as training dataset and experimenting with a variety of model-related parameters (i.e., Batch size, Epochs), this study achieved high-accuracy results in recognition of the garbage in the study site. Conclusively, the additional dataset of PET for training, with the similar GSD as test dataset, can improve the Recall value. Nevertheless, without combining with Original dataset collected from the study site, it is difficult to detect the PET using additional dataset only. Thus, combination of Original and additional dataset is a relatively better method to improve the Recall value of detecting PET.