This work presents a comprehensive study on fish detection in underwater environments using sonar images from the Caltech Fish Counting Dataset (CFC). We use the CFC dataset, initially designed for tracking purposes, to optimize and evaluate the performance of YOLO v7 and v8 models in fish detection. Our findings demonstrate a high performance of these deep learning models to accurately detect fish species in sonar images.
In our evaluation, YOLO v7 achieved an average precision of 68.3% (AP50) and 62.15% (AP75), while YOLO v8 demonstrated even a better performance with an average precision of 72.47% (AP50) and 66.21% (AP75) across the test dataset of 334,017 images. These high precision results underscore the effectiveness of these models in fish detection tasks under various underwater conditions.
With a dataset of 162,680 training images and 334,017 test images, our evaluation provides valuable insights into the models' performance and generalization across diverse underwater conditions. This study contributes to the advancement of underwater fish detection by showcasing the suitability of the CFC dataset and the efficacy of YOLO v7 and v8 models. These insights can pave the way for further advancements in fish detection, supporting conservation efforts and sustainable fisheries management.