Marine Trash Detection Using Deep Learning Models

Abstract

Underwater object detection faces various challenges like turbidity, non-uniform lighting, scattering, and lack of image clarity.The application of deep learning techniques for underwater object detection has significantly improved the detection performance as compared to the traditional object detection methods.However not many attempts have been made to detect trash from underwater images mostly due to lack of images and labeled ground truth datasets.This chapter aims to evaluate the YOLACT and Faster-RCNN algorithm to perform trash detection in an underwater environment.A freely available dataset of marine debris is used to train the CNN for object detection.The trained network is then evaluated on test images to assess its fitness for real-time applications.