Abstract
We present a survey on marine object detection based on deep neural network approaches, which are state-of-the-art approaches for the development of autonomous ship navigation, maritime surveillance, shipping management, and other intelligent transportation system applications in the future. The fundamental task of maritime transportation surveillance and autonomous ship navigation is to construct a reachable visual perception system that requires high efficiency and high accuracy of marine object detection. Therefore, high-performance deep learning-based algorithms and high-quality marine-related datasets need to be summarized. This survey focuses on summarizing the methods and application scenarios of maritime object detection, analyzes the characteristics of different marine-related datasets, highlights the marine detection application of the YOLO series model, and also discusses the current limitations of object detection based on deep learning and possible breakthrough directions. The large-scale, multiscenario industrialized neural network training is an indispensable link to solve the practical application of marine object detection. A widely accepted and standardized large-scale marine object verification dataset should be proposed.
Highlights
Information technology and intelligent development have changed the operation mode and direction of many industries
As a major advance in machine learning over the last decades, the deep learning approach is becoming the most powerful technique for intelligent transportation system [2]. e deep learning methodologies are applied in various fields in the maritime industry such as ship classification, object detection, collision avoidance, risk perception, and anomaly detection. e main application directions can be summarized as maritime surveillance and autonomous ship navigation
Liu et al [77] improved the YOLOv3 anchor method and feature fusion structure, respectively, GIOU loss was added to the loss function, and cross PANet was proposed to replace the feature pyramid network (FPN) structure in YOLOv3. e results show that the proposed method can significantly improve the accuracy of YOLOv3 detecting sea surface objects. e SeaBuoys dataset was established according to the actual sea surface conditions, and comparative experiments were carried out with the existing SeaShips dataset [78]
Summary
Information technology and intelligent development have changed the operation mode and direction of many industries. Deep learning-based object detection models still have to solve the three problems of region selection, feature extraction, and classification regression Speaking, it can be divided into two categories: single-stage methods and multistage methods. Chen et al [71] presented a novel hybrid deep learning algorithm that combines improved generative adversarial network (GAN) and CNN-based detection methods for small ship detection. It uses Gaussian Mixture Wasserstein GAN with gradient penalty to generate sufficient informative artificial samples of small ships and uses raw and generated data to approach high accuracy tiny object detection. Using rotation to enhance the dataset causes errors in object detection tasks, Dong et al [74] proposed a multiangle box-based rotation insensitive object detection structure (MRI-CNN) that improves the robustness of the model and reduces the detection performance impact due to the insufficient dataset
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