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

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.