In this study, a multi-sensor data fusion system using a load cell and vision sensor was considered in the development of a flatfish classifier for systematic fish management in aquaculture. In the single-sensor measurement method, each sensor has disadvantages. A load cell shows high performance in the measurement of adult fish, but the measurement of fry is affected significantly due to water weight (water weight disturbance). A vision sensor shows high performance in the measurement of fry, but the movement of fish (movement disturbance) affects the accurate measurement of adult fish. Therefore, in this study, these disturbances were compensated for using a datafusion algorithm, of which the performance was evaluated by a comparison between single sensor measurements and multi-sensor data fusion results.

在这项研究中，在开发用于水产养殖系统鱼类管理的比目鱼分类器时，考虑了使用称重传感器和视觉传感器的多传感器数据融合系统。在单传感器测量方法中，每个传感器都有缺点。称重传感器在成鱼的测量中表现出很高的性能，但是由于水重（水重扰动），鱼苗的测量受到显着影响。视觉传感器在鱼苗的测量中表现出很高的性能，但是鱼的运动（运动干扰）会影响成鱼的精确测量。因此，在这项研究中，使用数据融合算法对这些干扰进行了补偿，该算法通过比较单传感器测量结果与多传感器数据融合结果来评估其性能。