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Evaluating deep semantic segmentation networks for object detection in maritime surveillance
Maritime surveillance is important for applications in safety and security, but the visual detection of objects in maritime scenes remains challenging due to the diverse and unconstrained nature of such environments, and the need to operate in near real-time. Recent work on deep neural networks for semantic segmentation has achieved good performance in the road/urban scene parsing task. Driven by the potential application in autonomous vehicle navigation, many of the architectures are designed to be fast and lightweight. In this paper, we evaluate semantic segmentation networks in the context of an object detection system for maritime surveillance. Using data from the ADE20k scene parsing dataset, we train a selection of recent semantic segmentation network architectures to compare their performance on a number of publicly available maritime surveillance datasets
Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review
Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle
sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and
foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object
detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages
and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image
types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In
particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and
compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of
these approaches. The arti