基于深度学习的前视MBES图像中鱼类分割
Deep Learning based Segmentation of Fish in Noisy Forward Looking MBES Images
摘要：
在这项工作中，我们研究了一种深度学习（DL）方法，用于在由前视多波束回声测深仪（MBES）生成的噪声低分辨率图像的小数据集中进行鱼类分割。我们建立在DL和卷积神经网络（CNN）的语义分割的最新进展，并展示了一个端到端的方法，鱼/非鱼的概率预测的所有距离方位角位置投影的成像声纳。我们使用自收集的数据集，从丹麦的声音和法罗群岛训练和测试我们的模型和本技术，以获得令人满意的性能和泛化，即使是低容量的数据集。我们表明，我们的模型证明了所需的性能，并学会了利用语义上下文的重要性，并考虑到这一点，从真正的目标分离噪声和非目标。此外，我们提出了在低成本嵌入式平台上部署模型的技术，以获得适合边缘环境的更高性能-其中计算和功率受到尺寸/成本的限制-用于测试和原型设计。
英文摘要：
In this work, we investigate a Deep Learning (DL) approach to fish segmentation in a small dataset of noisy low-resolution images generated by a forward-looking multibeam echosounder (MBES). We build on recent advances in DL and Convolutional Neural Networks (CNNs) for semantic segmentation and demonstrate an end-to-end approach for a fish/non-fish probability prediction for all range-azimuth positions projected by an imaging sonar. We use self-collected datasets from the Danish Sound and the Faroe Islands to train and test our model and present techniques to obtain satisfying performance and generalization even with a low-volume dataset. We show that our model proves the desired performance and has learned to harness the importance of semantic context and take this into account to separate noise and non-targets from real targets. Furthermore, we present techniques to deploy models on low-cost embedded platforms to obtain higher performance fit for edge environments - where compute and power are restricted by size/cost - for testing and prototyping.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
