An enhanced YOLOv5 model for fish species recognition from underwater environments

Chiranjibi Shah; Simegnew Yihunie Alaba; M. M. Nabi; Jack Prior; Matthew Campbell; Farron Wallace; John E. Ball; Robert Moorhead

doi:10.1117/12.2663408

12 June 2023 An enhanced YOLOv5 model for fish species recognition from underwater environments

Chiranjibi Shah, Simegnew Yihunie Alaba, M. M. Nabi, Jack Prior, Matthew Campbell, Farron Wallace, John E. Ball, Robert Moorhead

Author Affiliations +

Proceedings Volume 12543, Ocean Sensing and Monitoring XV; 125430O (2023) https://doi.org/10.1117/12.2663408
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Species recognition is an important aspect of video based surveys, which support stock assessments, inspecting the ecosystem, handling production management, and protecting endangered species. It is a challenging task to implement fish species detection algorithms in underwater environments. In this work, we introduce the YOLOv5 model for the recognition of fish species that can be implemented as an object detection model for analyzing multiple fishes in a single image. Moreover, we have modified the depth scale of different layers in the backbone of the YOLOv5 model to obtain improved results on fish species recognition. In addition, we have implemented a transformer block in the backbone network and introduced a class balance loss function to obtain enhanced performance. It can perform fish species recognition as an object detection approach by classifying each of the fish species in addition to localizing for the estimation of the position and size of the fish in an image. Experiments are conducted on the fine-grained and large-scale reef fish dataset that we have obtained from the Gulf of Mexico – the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21). The experimental results demonstrate that an enhanced YOLOv5 model can yield better detection results in comparison to YOLOv5 for underwater fish species recognition.

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Citation Download Citation

Chiranjibi Shah, Simegnew Yihunie Alaba, M. M. Nabi, Jack Prior, Matthew Campbell, Farron Wallace, John E. Ball, and Robert Moorhead "An enhanced YOLOv5 model for fish species recognition from underwater environments", Proc. SPIE 12543, Ocean Sensing and Monitoring XV, 125430O (12 June 2023); https://doi.org/10.1117/12.2663408

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