Lyu, Hongguang; Shao, Zeyuan; Cheng, Tao; Yin, Yong; Gao, Xiaowei; (2022) Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review. IEEE Intelligent Transportation Systems Magazine pp. 2-27. 10.1109/mits.2022.3198334. (In press).

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Abstract

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 artic

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