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Convergence of Stereo Vision-Based Multimodal YOLOs for FasterDetection of Potholes

Computers, Materials & Continua 2022年第11期73卷 2821-2834页

作者： Sungan Yoon jeongho cho Department of Electrical Engineering Soonchunhyang UniversityAsan31538Korea

Road potholes can cause serious social issues,such as unexpected damages to vehicles and traffic *** efficient road management,technologies that quickly find potholes are required,and thus researches on such technologies have been conducted *** three-dimensional(3D)reconstruction method has relatively high accuracy and can be used in practice but it has limited application owing to its long data processing time and high sensor maintenance *** two-dimensional(2D)vision method has the advantage of inexpensive and easy application of ***,although the 2D vision method using the convolutional neural network(CNN)has shown improved pothole detection performance and adaptability,large amount of data is required to sufficiently train the ***,we propose a method to improve the learning performance of CNN-based object detection model by artificially generating synthetic data similar to a pothole and enhancing the learning ***,to make the defective areas appear more contrasting,the transformed disparity map(TDM)was calculated using stereo-vision cameras,and the detection performance of the model was further improved through the late fusion with RGB(Red,Green,Blue)***,through the convergence of multimodal You Only Look Once(YOLO)frameworks trained by RGB images and TDMs respectively,the detection performance was enhanced by 10.7%compared with that when using only ***,the superiority of the proposed method was confirmed by showing that the data processing speed was two times faster than the existing 3D reconstruction method.

关键词： CNN YOLO disparity map stereo vision pothole

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Reinforced CNN Forensic Discriminator to Detect Document Forgery by DCGAN

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Computers, Materials & Continua 2022年第6期71卷 6039-6051页

作者： Seo-young Lim jeongho cho Department of Electrical Engineering Soonchunhyang UniversityAsan31538Korea

Recently,the technology of digital image forgery based on a generative adversarial network(GAN)has considerably improved to the extent that it is difficult to distinguish it from the original image with the naked eye by compositing and editing a person’s face or a specific part with the original ***,much attention has been paid to digital image forgery as a social ***,document forgery through GANs can completely change the meaning and context in a document,and it is difficult to identify whether the document is forged or not,which is ***,few studies have been conducted on document forgery and new forgery-related attacks have emerged ***,in this study,we propose a novel convolutional neural network(CNN)forensic discriminator that can detect forged text or numeric images by GANs using CNNs,which have been widely used in image classification for many *** strengthen the detection performance of the proposed CNN forensic discriminator,CNN was trained after image preprocessing,including salt and pepper as well asGaussian ***,we performed CNN optimization to make existing CNN more suitable for forged text or numeric image detection,which have mainly focused on the discrimination of forged faces to *** test evaluation results using Hangul texts and numbers showed that the accuracy of forgery discrimination of the proposed method was significantly improved by 20%in Hangul texts and 5%in numbers compared with that of existing state-of-the-art methods,which proved the proposed model performance superiority and verified that it could be a useful tool in reducing crime potential.

关键词： Digital forensics CNN GAN discriminator image processing

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