Oceanographic Object Detection with YOLOv3

In this paper, the author uses YOLO (You Only Look Once) v3 network for oceanographic
image processing and data analysis to propose an underwater and drone object detection
system. This is mainly to address the ocean waste contamination that continuously
threatens species of marine life. The object detection model is built on top of Darknet, an
open source convolution network that makes it easy for different object detection models
to be created, trained, and executed. To be extremely fast and accurate, YOLO v3 is
graphed. Accordingly, you can easily switch between the model's speed and accuracy by
simply changing the model's size without needing any retraining. This research also
focuses on the reliability and accuracy of object detection models for testing and
documenting their output while inputting video and photographs taken with drones both
underwater and air. This is important when objects are to be accurately identified without
triggering false detection.