Implementation of Image Recognition for Human detection in Underwater Images

Ayush Aditya; Om Prakash; Praveen; Yash Rathi; Prof. Ramya K

doi:10.47392/IRJAEH.2024.0001

Authors

Ayush Aditya UG-Artificial Intelligence and Machine Learning Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India. Author
Om Prakash UG-Artificial Intelligence and Machine Learning Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India. Author
Praveen UG-Artificial Intelligence and Machine Learning Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India. Author
Yash Rathi UG-Artificial Intelligence and Machine Learning Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India. Author https://orcid.org/0009-0000-0453-2539
Prof. Ramya K Assistant Professor, Artificial Intelligence and Machine Learning Engineering, Dayananda Sagar College of Engineering, Bangalore, Karnataka, India. Author

DOI:

https://doi.org/10.47392/IRJAEH.2024.0001

Keywords:

Underwater object detection, Fish recognition, Region-based object detectors, Composite connection backbone, Speed, Seagrass detection, Positional Encoding

Abstract

Recent advances in deep learning have resolved the challenges of detection of objects underwater. Specialized methods have been developed as a result of the particular characteristics of small, fuzzy objects and heterogeneous noise. The Sample-Weighted Network (SWIPE Net) for small object recognition is one of them, as are frameworks with feature enhancement and anchor refining. Additionally, upgraded versions of the attention processes and YOLOv7 have been released. These advancements help with tracking the effects of clean energy technologies, developing accurate and reliable underwater object detection systems, bridging the communication gap between the deaf and hearing-impaired, and automating the analysis of underwater imagery for the extraction of ecological data.