Improved YOLOv5 Based Deep Learning System for Jellyfish Detection

Abstract

Massive jellyfish outbreaks have put human lives and marine ecosystems in great danger. As a result, the jellyfish detection methods have drawn a lot of attention, following two directions optical and sonar imaging. This work focuses on using optical imagery and CNN-based deep-learning object detection models to detect jellyfish. While labeled data of jellyfish play an important part in training deep learning models, there are a few open and available labeled datasets. Hence, we create our dataset to train these models using our model-assisted labeling method with over 11 thousand images of underwater jellyfish and corresponding annotation files in PASCAL VOC format. Our model-assisted labeling method saves the work of classical manual labeling by 70 percent, which is developed into application with YOLOv5. However, the YOLOv5 baseline suffers from the trade-off between real-time performance and low accuracy. Hence, an improved YOLOv5-nano is introduced based on adding GAM and replacing conventional Conv with CoordCov modules into the backbone of the conventional structure. The experiment results show that our improved model increases the accuracy of the conventional one by 1.3% and outperforms others including RetinaNet, SSD, Faster R-CNN, YOLOv6, and YOLOv8 at 89.1% [email protected]. On generalization performance, we verify the effectiveness of our work by conducting a test set of 15 different types of jellyfish with various shapes, colors, resolutions, and backgrounds. To conclude, our work establishes a comprehensive system from labeling the data, improving object detectors, and developing a feasible real-time jellyfish detector.

Keywords

• Jellyfish detection
• deep learning
• YOLOv5
• coordinate attention
• GAM
• CoordCov