Object Detection Based on Saliency and Sea-Sky Line for USV Vision

Shi, Junnan; Jin, Jiucai; Zhang, Jie

doi:10.1109/itoec.2018.8740763

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…Shi et al. [124] extracted multiscale feature information and fused it to improve ship detection accuracy. They used the CNN network to extract high‐level features and used Gabor‐based MS‐CLBP (multiscale completed local binary pattern) to extract low‐level features, finally fusing them.…”

Section: Challenges and Trendsmentioning

confidence: 99%

A review of intelligent ship marine object detection based on RGB camera

Yang,

Solihin,

Zhao

et al. 2023

IET Image Processing

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The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost‐effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi‐feature extraction, multi‐fusion technology, and lightweight detection architecture.

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Section: Challenges and Trendsmentioning

confidence: 99%

A review of intelligent ship marine object detection based on RGB camera

Yang,

Solihin,

Zhao

et al. 2023

IET Image Processing

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“…Zeng et al [4] enhanced the Mean-Shift segmentation approach for extracting the water target and tracked the target's position using the Kalman filter. J. Shi et al [5] recognized the ship target by using the frequency-tune saliency extraction algorithm and followed it using the correlation filter approach for joint location and scale estimation. However, the correlation filtering method makes real-time tracking in occlusion challenging.…”

Section: Related Workmentioning

confidence: 99%

Water Target Recognition Method and Application for Unmanned Surface Vessels

et al. 2022

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Water target recognition is a critical challenge for the perception technology of unmanned surface vessels (USVs). In the application of USV, detection accuracy and the inference time both matter, while it is tough to strike a balance and single-frame water target detection behaves unstable in the video detection. To solve these problems, many strategies are applied to increase YOLOv4's performance, including network pruning, the focal loss function, blank label training, and preprocessing with histogram normalization. The optimized detection method achieves a mean average precision (mAP) of 81.74% and a prediction speed of 26.77 frames per second (FPS), which meets the USV navigation requirements. To build the integrated USV-based system for water target recognition, a water target dataset containing 9936 images is created from offshore USV experiments in which the human-in-the-loop annotation and mosaic data augmentation methods are used. The issues of miss detection and false alarm can be considerably mitigated by cascading the Siamese-RPN tracking network, and the major color of a water target can be retrieved using a local contrast saliency color detection scheme. The system being tested is called "ME120" includes an embedded edge computing platform (Nvidia Jetson AGX Xavier). Finally, online dataset learning demonstrates the improved YOLOv4 achieves an increase of 66.98% in FPS at the cost of a decrease of 0.79% in mAP when compared with the original YOLOv4 and offline navigation experiments validate that our system achieves high recognition capability while maintaining a high degree of robustness.

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“…This division helps isolate the area under observation which eventually decreases the computational cost associated with any further image processing task. For this reason, detection of the horizon line is considered a critical preprocessing step for maritime computer applications related to the navigation of unmanned aerial vehicles [1,2] and surface ships [3,4], collision avoidance [5,6], object detection [7] and tracking [8], surveillance [9], distance estimation [10], and video stabilization [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Visual-Range Sea Image Dataset for Sea Horizon Line Detection in Changing Maritime Scenes

Hashmani

Umair

2022

JMSE

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Sea horizon line (SHL) detection plays a pivotal role in the computational performance improvement of computer applications for the maritime environment by dividing the image into sea and sky regions. This division isolates the region of interest and reduces the computational cost of further processing. Testing and performance evaluation of SHL detection methods require a robust image dataset covering the maritime environment’s features at different geographical, seasonal, and maritime conditions. However, publicly available maritime image datasets are developed under a limited environment with slight-to-moderate variations in maritime features. This article proposes a novel sea image dataset that fills this gap by incorporating various geographical, seasonal, and maritime features. Across West Malaysia, one offshore and four geographically separated onshore locations were selected. On ten different occasions, field observations were recorded using a visual-range optical sensor and weather station. The data collection experiments were conducted between February 2020 until April 2021. The collected data were preprocessed and SHL images were selected based on their high feature diversity. Manual SHL annotation was applied on images, and a ground truth matrix was generated, which serves as a performance benchmark for SHL detection methods. As a result, the dataset presents 2673 high-definition (1920 × 1080 pixels) RGB images having a combination of 36 different geographical, seasonal, and maritime features to test and evaluate computer vision-based SHL detection methods.

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Object Detection Based on Saliency and Sea-Sky Line for USV Vision

Cited by 10 publications

References 13 publications

A review of intelligent ship marine object detection based on RGB camera

A review of intelligent ship marine object detection based on RGB camera

Water Target Recognition Method and Application for Unmanned Surface Vessels

A Novel Visual-Range Sea Image Dataset for Sea Horizon Line Detection in Changing Maritime Scenes

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