Computer-vision-based bare-hand augmented reality interface for controlling an AR object

Shrestha, Deepanjal; Lee, Hyungwoo; Chun, Junchul

doi:10.1504/ijcaet.2018.090527

Cited by 2 publications

(2 citation statements)

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“…For lossless images, the PSNR is not defined because the mean squared error is 0. Equation (4) represents PSNR.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…This is a technology being used in various fields such as robots, medical treatment, security, games, and road transportation by using images and images through deep learning as information technology develops. The goal of computer vision is to extract interesting information from pattern recognition, statistical learning, and projection geometry through object detection, segmentation, and recognition in images [3,4]. The object detection is a technology that distinguishes and identifies a specific object from the background in visual data such as images and videos [5].…”

Section: Introductionmentioning

confidence: 99%

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Pothole Classification Model Using Edge Detection in Road Image

Baek

Chung

2020

Applied Sciences

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Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.

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“…For lossless images, the PSNR is not defined because the mean squared error is 0. Equation (4) represents PSNR.…”

Section: Performance Evaluationmentioning

confidence: 99%