Artificial Neural Network for Glider Detection in a Marine Environment by Improving a CNN Vision Encoder

Abstract

Despite major economic and technological advances, much of the ocean remains unexplored, which has led to the use of remotely operated vehicles (ROVs) and gliders for surveying. ROVs and underwater gliders are essential for ocean data collection. Gliders, which control their own buoyancy, are particularly effective unmanned platforms for long-term observations. The traditional method of recovering the glider on a small boat is a risky operation and depends on the skill of the workers. Therefore, a safer, more efficient, and automated system is needed to recover them. In this study, we propose a lightweight artificial neural network for underwater glider detection that is efficient for learning and inference. In order to have a smaller parameter size and faster inference, a convolutional neural network (CNN) vision encoder in an artificial neural network splits an image of a glider into a number of elongated patches that overlap to better preserve the spatial information of the pixels in the horizontal and vertical directions. Global max-pooling, which computes the maximum over all the spatial locations of an input feature, was used to activate the most salient feature vectors at the end of the encoder. As a result of the inference of the glider detection models on the test dataset, the average precision (AP), which indicates the probability that an object is located within the predicted bounding box, shows that the proposed model achieves AP = 99.7%, while the EfficientDet-D2 model for comparison of detection performance achieves AP = 69.2% at an intersection over union (IOU) threshold of 0.5. Similarly, the proposed model achieves an AP of 78.9% and the EfficientDet-D2 model achieves an AP of 50.5% for an IOU threshold of 0.75. These results show that accurate prediction is possible within a wide range of recall for glider position inference in a real ocean environment.