Object Recognition in Pond Environments Using Deep Learning

Complicated underwater environment, such as visibility limitations and illumination conditions pose significant challenges for underwater imaging and its object recognition performance. These issues are especially critical for applications involving autonomous underwater vehicles (AUVs) or robotic systems involved in object recognition tasks during search-and-retrieval operations. Moreover, high-turbidity underwater image datasets, especially for pond environments, remain scarce. Therefore, this study focuses on establishing a pond underwater images dataset and evaluating the deep learning-based object recognition architecture, You Only Look Once Version 5 (YOLOv5), in recognizing multiple objects in respective underwater pond images. The dataset contains self-captured 1116 underwater pond images, which are annotated with LabelImg for object recognition and dataset generation. Under varying depths, camera distances, and object angles, the YOLOv5 reaches a mean accuracy mAP 50-95 of 87.96%, demonstrating its effectiveness for recognizing multiple objects in pond underwater environments.