College of Marine Science, Shanghai Ocean University, Shanghai, China.
Shanghai Estuary Marine Surveying and Mapping Engineering Technology Research Center, Shanghai Ocean University, Shanghai, China.
J Fish Biol. 2024 Feb;104(2):422-432. doi: 10.1111/jfb.15349. Epub 2023 Mar 6.
Fish are a critical component of marine biology; therefore, the accurate identification and counting of fish are essential for the objective monitoring and assessment of marine biological resources. High-frequency adaptive resolution imaging sonar (ARIS) is widely used for underwater object detection and imaging, and it quickly obtains close-up video of free-swimming fish in high-turbidity water environments. Nonetheless, processing the massive data output using imaging sonars remains a major challenge. Here, the authors developed an automatic image-processing programme that fuses K-nearest neighbour background subtraction with DeepSort target tracking to automatically track and count fish. The automatic programme was evaluated using four test data sets with different target sizes and observation ranges and differently deployed sonars. According to the results, the approach successfully counted free-swimming fish targets with an accuracy index of 73% and a completeness index of 70%. Under appropriate conditions, this approach could replace time-consuming semi-automatic approaches and improve the efficiency of imaging sonar data processing, while providing technical support for future real-time data processing.
鱼类是海洋生物学的重要组成部分；因此，准确识别和计数鱼类对于客观监测和评估海洋生物资源至关重要。高频自适应分辨率成像声纳（ARIS）广泛应用于水下目标探测和成像，可快速获取高浊度水环境中自由游动鱼类的特写视频。然而，处理成像声纳的大量数据输出仍然是一个主要挑战。在这里，作者开发了一种自动图像处理程序，该程序将 K-最近邻背景减除与 DeepSort 目标跟踪相结合，以自动跟踪和计数鱼类。使用具有不同目标大小和观察范围以及不同部署声纳的四个测试数据集对自动程序进行了评估。结果表明，该方法能够成功地对自由游动的鱼类目标进行计数，准确率为 73%，完整性为 70%。在适当的条件下，这种方法可以代替耗时的半自动方法，提高成像声纳数据处理的效率，为未来的实时数据处理提供技术支持。
