CNN-based YOLOv3 Comparison for Underwater Object Detection

Object detection that deals with identifying and locating object is one of area that integrate from the advance- ment in machine learning and computer vision. Modern object detection which carried out supervised learning utilizes Convo- lutional Neural Network (CNN) as the backbone of the detection architecture which is significant for underwater object detection as the underwater images are usually low in quality and blurry. Single stage detection such as You Only Look Once (YOLO) is one the famous object detection model that is prominent among researchers due to high performance in accuracy and processing speed. However, YOLO has many versions where the current incremental improvement model of YOLOv3 has been widely used by researchers to solve different types of problem related to object detection. Therefore, there is a need to explore the trade-off relationship between the processing speed and precision of each YOLO model. In the study, two different open source underwater datasets were used in four different YOLOv3 models namely as YOLOv3-SPP, YOLOv3-Tiny, YOLOv3-Tiny-PRN and the original YOLOv3 in order to study their performance based on metrics evaluation of precision and processing speed (FPS). The result shows that YOLOv3-SPP proved to be the best in terms of precision while YOLOv3-Tiny-PRN lead in terms of execution speed. So, this study shows that YOLOv3 model is highly significant to be implemented and able to accurately detect underwater objects with haze and low-light environment. This study can help researchers and industry in determining the best YOLOv3 model specifically for detection of the underwater images and its application.