Vision based long range object detection and tracking for unmanned surface vehicle

Wang, Han; Mou, Xiaozheng; Mou, Wei; Yuan, Shenghai; Ulun, Soner; Yang, Shuai; Shin, Bok-Suk

doi:10.1109/iccis.2015.7274604

Cited by 23 publications

(12 citation statements)

References 12 publications

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“…This setup allows ranging by triangulation. In [33], object detection in a maritime environment is achieved at a range of 500 m. However, it is likely that much longer distances are accessible with suitable optics and methodology.…”

Section: Visual Sensorsmentioning

confidence: 99%

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Thombre

Zhao

Ramm-Schmidt³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

151

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Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remotesensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems.

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Section: Visual Sensorsmentioning

confidence: 99%

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Thombre

Zhao

Ramm-Schmidt³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

151

View full text Add to dashboard Cite

show abstract

“…Afterward, the obstacles were detected using saliency values [1], [2]. Furthermore, Wang et al extended a single vision sensor into a stereo vision sensor to enhance the obstacle detection performance [3] and adopted an HD high-resolution vision sensor so that obstacles more than 200 m away could be recognized [4], [5]. Likewise, Oren used the canny edge detection method to find the horizon in order to reduce the amount of computation and detected interest points of obstacles through the thinning computation of areas where the pixel values changed [6].…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Real-Time Obstacle Segmentation Algorithm for Autonomous Surface Vehicle

Kim¹,

Myung

Kim³

et al. 2019

IEEE Access

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Among various sensors used to recognize obstacles in marine environments, vision sensors are the most basic. Vision sensors are significantly affected by the surrounding environment and cannot recognize distant objects. However, despite these drawbacks, they can detect objects that radars cannot detect in nearby regions. They can also recognize small obstacles such as boats that are not equipped with an automatic identification system (AIS) or buoys. Thus, vision sensors and radar can be used in a complementary manner. This paper proposes a vision sensor-based model, called Skip-ENet, for recognizing obstacles in real time. Compared with ENet, the amount of computation is not significantly higher. Further, Skip-ENet can segment complex marine obstacles effectively by increasing the values for the class accuracy and mean Intersection of Union (mIoU). Moreover, this model enables even low-cost embedded systems to compute 10 or more frames per second (fps). The superiority of the proposed model was verified by comparing its performance with that of the conventional segmentation models, MobileNet, ENet, and DeeplabV3+.

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“…In our previous work [ 1 , 2 , 3 , 4 ], we built a binocular stereo vision system with a very large baseline B and focal length f , and a ranging ability within 500 m was achieved. However, increasing the ranging ability of the binocular stereo vision system will result in an even bulkier structure on the boat and a problem for calibration.…”

Section: Introductionmentioning

confidence: 99%

Wide-Baseline Stereo-Based Obstacle Mapping for Unmanned Surface Vehicles

Mou

Wang

2018

Sensors

Self Cite

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This paper proposes a wide-baseline stereo-based static obstacle mapping approach for unmanned surface vehicles (USVs). The proposed approach eliminates the complicated calibration work and the bulky rig in our previous binocular stereo system, and raises the ranging ability from 500 to 1000 m with a even larger baseline obtained from the motion of USVs. Integrating a monocular camera with GPS and compass information in this proposed system, the world locations of the detected static obstacles are reconstructed while the USV is traveling, and an obstacle map is then built. To achieve more accurate and robust performance, multiple pairs of frames are leveraged to synthesize the final reconstruction results in a weighting model. Experimental results based on our own dataset demonstrate the high efficiency of our system. To the best of our knowledge, we are the first to address the task of wide-baseline stereo-based obstacle mapping in a maritime environment.

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Vision based long range object detection and tracking for unmanned surface vehicle

Cited by 23 publications

References 12 publications

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Vision-Based Real-Time Obstacle Segmentation Algorithm for Autonomous Surface Vehicle

Wide-Baseline Stereo-Based Obstacle Mapping for Unmanned Surface Vehicles

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