Judgement of Tear of Fish Farming Nets using Deep Learning

Abstract

Damage in fish farming nets can lead to serious losses and/or adverse environmental impact. Nonetheless, detecting such damage is challenging. Human experts could inspect the nets, but this process is costly and time-consuming. Alternatively, remotely operated underwater vehicles (ROV) can be used to inspect the fishnets. By using advanced deep-learning techniques for autonomous navigation and object detection, fishnets can be inspected efficiently while minimizing human intervention. In this paper, a deep convolutional neural networks (CNN) is employed to classify images of torn and normal fishnets. Training deep CNN models requires numerous image data, whereas a limited amount of fishnet images are available. To resolve the dearth of available data, data-augmentation techniques are adopted to generate images of torn and normal fishnets. The trained CNN model shows high accuracy for classifying the given augmented test dataset.