Automated fish detection and classification on sonar images using detection transformer and YOLOv7

Ella Mahoro; Moulay A. Akhloufi

doi:10.1117/12.2688330

28 July 2023 Automated fish detection and classification on sonar images using detection transformer and YOLOv7

Ella Mahoro, Moulay A. Akhloufi

Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 1274903 (2023) https://doi.org/10.1117/12.2688330
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France

Abstract

In order to maintain a healthy ecosystem and fish stocks, it is necessary to monitor the abundance and frequency of fish species. In this article, we propose a fish detection and classification system. In the first step, the images were extracted from a public Ocqueoc River DIDSON high-resolution imaging sonar dataset and annotated. End-to-end object detection models, Detection Transformer with a ResNet-50 backbone (DETR-ResNet-50) and YOLOv7 were used to detect and classify fish species. With a mean average precision of 0.79, YOLOv7 outperformed DETR-ResNet-50. The results demonstrated that the proposed system can in fact be used to detect and classify fish species using high-resolution imaging sonar data.

Citation Download Citation

Ella Mahoro and Moulay A. Akhloufi "Automated fish detection and classification on sonar images using detection transformer and YOLOv7", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 1274903 (28 July 2023); https://doi.org/10.1117/12.2688330

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KEYWORDS

Data modeling

Performance modeling

Video

Autoregressive models

Object detection

Transformers

Education and training

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