AI Article Synopsis

  • The research compares Faster R-CNN and YOLOv8 for detecting fishing vessels and fish in real-time, emphasizing their significance in improving fisheries monitoring through deep learning techniques.
  • By reviewing existing literature, the study highlights the current advancements and challenges in object detection specific to fisheries management, identifying gaps in previous methods.
  • The findings reveal that YOLOv8 demonstrates superior detection accuracy, showcasing its potential benefits for enhancing maritime surveillance and protecting marine resources.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11157610PMC
http://dx.doi.org/10.7717/peerj-cs.2033DOI Listing

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