Automatic Detection of Disaster-Causing Organisms near the Waters of Nuclear Power Plant Based on LiveScope Scanning Sonar Images

Abstract

Nuclear power serves as an efficient, clean, and low-carbon energy source that constitutes a significant component of the energy portfolio in numerous countries. Most nuclear power plants are predominantly situated in coastal regions, utilizing seawater as the ultimate heat sink for their cooling systems. Real-time monitoring of marine organism dynamics near water intakes is essential to mitigate the risk of unit shutdowns triggered by outbreaks of disaster-causing organisms (DCOs). This study employed LiveScope scanning sonar videos captured near the Ningde Nuclear Power Plant to develop a dataset for detecting the light spot area of the DCOs. We proposed a directionally optimized model, Bio-YOLO v7, which significantly enhances the detection of small targets in sonar images. The Bio-YOLO v7 model achieved precision, recall, and average precision rates of 85.29%, 83.28%, and 81.49%, respectively, demonstrating superior performance in identifying DCOs near the intakes of nuclear power plant. The light spot size of the DCOs exhibited significant periodic variations, serving as a crucial indicator for forecasting outbreak events.

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