• Title/Summary/Keyword: R-CNN

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Crack Detection on the Road in Aerial Image using Mask R-CNN (Mask R-CNN을 이용한 항공 영상에서의 도로 균열 검출)

  • Lee, Min Hye;Nam, Kwang Woo;Lee, Chang Woo
    • Journal of Korea Society of Industrial Information Systems
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    • v.24 no.3
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    • pp.23-29
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    • 2019
  • Conventional crack detection methods have a problem of consuming a lot of labor, time and cost. To solve these problems, an automatic detection system is needed to detect cracks in images obtained by using vehicles or UAVs(unmanned aerial vehicles). In this paper, we have studied road crack detection with unmanned aerial photographs. Aerial images are generated through preprocessing and labeling to generate morphological information data sets of cracks. The generated data set was applied to the mask R-CNN model to obtain a new model in which various crack information was learned. Experimental results show that the cracks in the proposed aerial image were detected with an accuracy of 73.5% and some of them were predicted in a certain type of crack region.

Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis

  • Rini, Widyaningrum;Ika, Candradewi;Nur Rahman Ahmad Seno, Aji;Rona, Aulianisa
    • Imaging Science in Dentistry
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    • v.52 no.4
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    • pp.383-391
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    • 2022
  • Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics(i.e., dice coefficient and intersection-over-union [IoU] score). MultiLabel U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.

A Study on Automatically Information Collection of Underground Facility Using R-CNN Techniques (R-CNN 기법을 이용한 지중매설물 제원 정보 자동 추출 연구)

  • Hyunsuk Park;Kiman Hong;Yongsung Cho
    • Journal of the Society of Disaster Information
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    • v.19 no.3
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    • pp.689-697
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    • 2023
  • Purpose: The purpose of this study is to automatically extract information on underground facilities using a general-purpose smartphone in the process of applying the mini-trenching method. Method: Data sets for image learning were collected under various conditions such as day and night, height, and angle, and the object detection algorithm used the R-CNN algorithm. Result: As a result of the study, F1-Score was applied as a performance evaluation index that can consider the average of accurate predictions and reproduction rates at the same time, and F1-Score was 0.76. Conclusion: The results of this study showed that it was possible to extract information on underground buried materials based on smartphones, but it is necessary to improve the precision and accuracy of the algorithm through additional securing of learning data and on-site demonstration.

Deep Learning-based Rail Surface Damage Evaluation (딥러닝 기반의 레일표면손상 평가)

  • Jung-Youl Choi;Jae-Min Han;Jung-Ho Kim
    • The Journal of the Convergence on Culture Technology
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    • v.10 no.2
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    • pp.505-510
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    • 2024
  • Since rolling contact fatigue cracks can always occur on the rail surface, which is the contact surface between wheels and rails, railway rails require thorough inspection and diagnosis to thoroughly inspect the condition of the cracks and prevent breakage. Recent detailed guidelines on the performance evaluation of track facilities present the requirements for methods and procedures for track performance evaluation. However, diagnosing and grading rail surface damage mainly relies on external inspection (visual inspection), which inevitably relies on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we conducted a deep learning model study for rail surface defect detection using Fast R-CNN. After building a dataset of rail surface defect images, the model was tested. The performance evaluation results of the deep learning model showed that mAP was 94.9%. Because Fast R-CNN has a high crack detection effect, it is believed that using this model can efficiently identify rail surface defects.

The Accident Risk Detection System in Dashcam Video using Object Detection Algorithm (물체 탐지 알고리즘을 활용한 블랙박스 영상 내 사고 위험 감지 시스템)

  • Hong, Jin-seok;Han, Myeong-woo;Kim, Jeong-seon;Kim, Kyung-sup
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2018.10a
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    • pp.364-368
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    • 2018
  • In this paper, we use Faster R-CNN that is one of object detection algorithm and OpenCV that purposes computer vision, to implement the system that can detect danger when a vehicle attempts to change lanes into its own lane in videos of highway, national road, general road and etc. Also, the performance of implemented system is evaluated to prove that the performance is not bad.

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A Study on Trademark Vienna Classification Automation Using Faster R-CNN and DenseNet (Faster R-CNN과 DenseNet을 이용한 도형 상표 비엔나 분류 자동화 연구)

  • Lee, Jin-woo;Kim, Hong-ki;Lee, Ha-young;Ko, Bong-soo;Lee, Bong-gun
    • Proceedings of the Korea Information Processing Society Conference
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    • 2019.10a
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    • pp.848-851
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    • 2019
  • 이미지 형식으로 등록되는 상표의 특성상 상표의 검색에는 어려움이 따른다. 특허청은 도형 상표의 검색을 용이하게 하기 위해 상표가 포함하고 있는 구성요소에 도형분류코드를 부여한다. 하지만 도형 상표에 포함된 이미지를 확인하고 분류코드를 부여하는 과정은 사람이 직접 수행해야 한다는 어려움이 따른다. 이에 본 논문에서는 딥러닝을 이용하여 자동으로 도형 상표 내 객체를 인식하고 분류코드를 부여하는 방안을 제안한다. DenseNet을 이용하여 중분류를 먼저 예측한 후 각 중분류에 해당하는 Faster R-CNN 모델을 이용하여 세분류 예측을 수행하였다. 성능평가를 통해 비엔나분류 중분류별 평균 74.49%의 예측 정확도를 확인하였다.

Road Damage Detection and Classification based on Multi-level Feature Pyramids

  • Yin, Junru;Qu, Jiantao;Huang, Wei;Chen, Qiqiang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.2
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    • pp.786-799
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    • 2021
  • Road damage detection is important for road maintenance. With the development of deep learning, more and more road damage detection methods have been proposed, such as Fast R-CNN, Faster R-CNN, Mask R-CNN and RetinaNet. However, because shallow and deep layers cannot be extracted at the same time, the existing methods do not perform well in detecting objects with fewer samples. In addition, these methods cannot obtain a highly accurate detecting bounding box. This paper presents a Multi-level Feature Pyramids method based on M2det. Because the feature layer has multi-scale and multi-level architecture, the feature layer containing more information and obvious features can be extracted. Moreover, an attention mechanism is used to improve the accuracy of local boundary boxes in the dataset. Experimental results show that the proposed method is better than the current state-of-the-art methods.

Atypical Character Recognition Based on Mask R-CNN for Hangul Signboard

  • Lim, Sooyeon
    • International journal of advanced smart convergence
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    • v.8 no.3
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    • pp.131-137
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    • 2019
  • This study proposes a method of learning and recognizing the characteristics that are the classification criteria of Hangul using Mask R-CNN, one of the deep learning techniques, to recognize and classify atypical Hangul characters. The atypical characters on the Hangul signboard have a lot of deformed and colorful shapes beyond the general characters. Therefore, in order to recognize the Hangul signboard character, it is necessary to learn a separate atypical Hangul character rather than the existing formulaic one. We selected the Hangul character '닭' as sample data and constructed 5,383 Hangul image data sets and used them for learning and verifying the deep learning model. The accuracy of the results of analyzing the performance of the learning model using the test set constructed to verify the reliability of the learning model was about 92.65% (the area detection rate). Therefore we confirmed that the proposed method is very useful for Hangul signboard character recognition, and we plan to extend it to various Hangul data.

Comparing U-Net convolutional network with mask R-CNN in Nuclei Segmentation

  • Zanaty, E.A.;Abdel-Aty, Mahmoud M.;ali, Khalid abdel-wahab
    • International Journal of Computer Science & Network Security
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    • v.22 no.3
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    • pp.273-275
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    • 2022
  • Deep Learning is used nowadays in Nuclei segmentation. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the exemplary model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN, in the nuclei segmentation task and find that they have different strengths and failures. we compared both models aiming for the best nuclei segmentation performance. Experimental Results of Nuclei Medical Images Segmentation using U-NET algorithm Outperform Mask R-CNN Algorithm.

Feature Extraction of Non-proliferative Diabetic Retinopathy Using Faster R-CNN and Automatic Severity Classification System Using Random Forest Method

  • Jung, Younghoon;Kim, Daewon
    • Journal of Information Processing Systems
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    • v.18 no.5
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    • pp.599-613
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    • 2022
  • Non-proliferative diabetic retinopathy is a representative complication of diabetic patients and is known to be a major cause of impaired vision and blindness. There has been ongoing research on automatic detection of diabetic retinopathy, however, there is also a growing need for research on an automatic severity classification system. This study proposes an automatic detection system for pathological symptoms of diabetic retinopathy such as microaneurysms, retinal hemorrhage, and hard exudate by applying the Faster R-CNN technique. An automatic severity classification system was devised by training and testing a Random Forest classifier based on the data obtained through preprocessing of detected features. An experiment of classifying 228 test fundus images with the proposed classification system showed 97.8% accuracy.