Overview
The identification of anomalies on the surface of fish is paramount for assessing their health status and vital signs. This information is essential for preventing fish disease outbreaks, anticipating changes in water quality, and enhancing fish welfare. The zebrafish (Danio Rerio), as a significant model organism, underscores the growing importance of intelligent management and detection methods for this species.
However, conventional fish disease detection methods fall short in terms of precision, non-invasive early warning systems, and real-time monitoring for small targets like zebrafish, potentially causing irreversible harm, particularly to smaller species. Our online detection method is designed to significantly improve the accuracy of detecting small targets and precisely classifying minute differences in detection subjects. Beyond the detection of small fish such as zebrafish, this research approach is highly flexible and can accommodate the need for real-time underwater monitoring of surface abnormalities in various fish species within aquaculture.
At present, our online platform focuses on detecting body surface abnormalities in zebrafish. To validate our methodology, the platform offers two interfaces for testing detection with images and videos. For those requiring real-time video detection, we recommend deploying the method framework provided by this platform on a local basis.
Should you have any inquiries regarding Fishsitter, please feel free to reach out to us at 964751655@qq.com.
Fishsitter: Fish disease detection online platform
