Analysis of the performance of Faster R-CNN and YOLOv8 in detecting fishing vessels and fishes in real time
Lotfi Ezzeddini, Jalel Ktari, Tarek Frikha, Naif Alsharabi, Abdulaziz M. Alayba, Abdullah J. Alzahrani, Amr Mohsen Jadi, Abdulsalam Alkholidi, Habib Hamam
Abstract
This research conducts a comparative analysis of Faster R-CNN and YOLOv8 for real-time detection of fishing vessels and fish in maritime surveillance. The study underscores the significance of this investigation in advancing fisheries monitoring and object detection using deep learning. With a clear focus on comparing the performance of Faster R-CNN and YOLOv8, the research aims to elucidate their effectiveness in real-time detection, emphasizing the relevance of such capabilities in fisheries management. By conducting a thorough literature review, the study establishes the current state-of-the-art in object detection, particularly within the context of fisheries monitoring, while discussing existing methods, challenges, and limitations. The findings of this study not only shed light on the superiority of YOLOv8 in precise detection but also highlight its potential impact on maritime surveillance and the protection of marine resources.