Demonstration of a c.c.d. image processor for two-dimensional edge detection

Nudd, G.R.; Nygaard, P.A.

doi:10.1049/el:19780057

Cited by 2 publications

(1 citation statement)

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“…The shift from traditional computer vision methods to deep learning in image recognition has been decisive. Traditional computer vision methods, such as the Sobel [2] and Canny [3] edge detection algorithms, identify object contours and edges by analyzing changes in image brightness. The Sobel algorithm highlights edges by calculating the gradient of image brightness, while the Canny algorithm further optimizes edge detection through a multi-stage process, enhancing accuracy and robustness.…”

Section: Introductionmentioning

confidence: 99%

StereoYOLO: A Stereo Vision-Based Method for Maritime Object Recognition and Localization

Shang,

Yu,

Zeng

et al. 2024

JMSE

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Image recognition is vital for intelligent ships’ autonomous navigation. However, traditional methods often fail to accurately identify maritime objects’ spatial positions, especially under electromagnetic silence. We introduce the StereoYOLO method, an enhanced stereo vision-based object recognition and localization approach that serves autonomous vessels using only image sensors. It is specifically refined for maritime object recognition and localization scenarios through the integration of convolutional and coordinated attention modules. The method uses stereo cameras to identify and locate maritime objects in images and calculate their relative positions using stereo vision algorithms. Experimental results indicate that the StereoYOLO algorithm boosts the mean Average Precision at IoU threshold of 0.5 (mAP50) in object recognition by 5.23%. Furthermore, the variation in range measurement due to target angle changes is reduced by 6.12%. Additionally, upon measuring the distance to targets at varying ranges, the algorithm achieves an average positioning error of 5.73%, meeting the accuracy and robustness criteria for maritime object collision avoidance on experimental platform ships.

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Section: Introductionmentioning

confidence: 99%