Detection of Road Cracks Using Convolutional Neural Networks and Threshold Segmentation

Ashraf, Arselan; Sophian, Ali; Shafie, Amir Akramin; Gunawan, Teddy Surya; Ismail, Norfarah Nadia; Bawono, Ali Aryo

doi:10.51662/jiae.v2i2.82

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Deep CNN has a significant advantage over traditional processing methods of images and videos. In contrast, Recurrent Neural Network (RNN) processes continuous data such as voice and text [11] [12]. Zhou et al [13] proposed a real-time 3DFR system that employs a trained two-level cascade classifier and preprocesses RGB and depth data.…”

Section: Literature Reviewmentioning

confidence: 99%

Learning a Multimodal 3D Face Embedding for Robust RGBD Face Recognition

Faizabadi

Zaki

Abidin

et al. 2023

JIAE

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Machine vision will play a significant role in the next generation of IR 4.0 systems. Recognition and analysis of faces are essential in many vision-based applications. Deep Learning provides the thrust for the advancement in visual recognition. An important tool for visual recognition tasks is Convolution Neural networks (CNN). However, the 2D methods for machine vision suffer from Pose, Illumination, and Expression (PIE) challenges and occlusions. The 3D Race Recognition (3DFR) is very promising for dealing with PIE and a certain degree of occlusions and is suitable for unconstrained environments. However, the 3D data is highly irregular, affecting the performance of deep networks. Most of the 3D Face recognition models are implemented from a research aspect and rarely find a complete 3DFR application. This work attempts to implement a complete end-to-end robust 3DFR pipeline. For this purpose, we implemented a CuteFace3D. This face recognition model is trained on the most challenging dataset, where the state-of-the-art model had below 95% accuracy. An accuracy of 98.89% is achieved on the intellifusion test dataset. Further, for open world and unseen domain adaptation, embeddings learning is achieved using KNN. Then a complete FR pipeline for RGBD face recognition is implemented using a RealSense D435 depth camera. With the KNN classifier and k-fold validation, we achieved 99.997% for the open set RGBD pipeline on registered users. The proposed method with early fusion four-channel input is found to be more robust and has achieved higher accuracy in the benchmark dataset.

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Section: Literature Reviewmentioning

confidence: 99%

Learning a Multimodal 3D Face Embedding for Robust RGBD Face Recognition

Faizabadi

Zaki

Abidin

et al. 2023

JIAE

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“…The impact of fossil fuel prices on economic growth, environment sustainability, employment policy, healthcare expenditure, urban development, retailer businesses, and renewable fuel standard compliance cost save also been studied [16,17,18,19,20,21,22,23]. Some studies investigated and explored the impact of fuel prices on inventory policies, electricity markets, spatial interactions, welfare impacts, fuel markets and inflations [24,25,26,27,28,29,30]. However, the impacts of fuel prices on lifestyles and travel behaviors have not been widely studied.…”

Section: Introductionmentioning

confidence: 99%

The effect of rising prices of subsidized fuel on the use of private cars

Yosritzal,

Anisa,

Putri

2024

JIAE

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The rising fuel prices on September 3, 2022, are expected to reduce the use of private cars and increase the use of public transport. This expectation has raised optimism among public transport operators and is expected to encourage them to invest more. However, there is no evidence to support the expectation. Therefore, it is essential to study the effect of the fuel price on the use of a particular transport mode, thus motivating this study. This paper aims to investigate the impact of the rising costs of subsidized fuel on private cars. A Likert-scale type of questionnaire was used to collect data. The data was analyzed descriptively, and then a conclusion was made based on the results. The study found that the daily commute of respondents changed after fuel prices increased. Distance, duration, and frequency of travel tend to be reduced and prioritized over primary activities. To reduce travel costs, the respondents who usually use cars are more likely to use motorcycles than public transport. The findings of this study disprove that an increase in fuel prices would increase the use of public transport. Why is public transport less favoured compared to motorcycles? It seems that the quality of service provided by public transport fails to satisfy the consumers' needs.

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“…In addition, there is a high possibility of errors in the calculation and classification of vehicles made by the surveyors because multiple vehicle classes need to be identified and counted simultaneously. Table 1 shows the vehicle classes, [3], that are used as detection classes in this study [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Counting Various Vehicles using YOLOv4 and DeepSORT

Kusumah,

Djayusman,

Setiadi

et al. 2023

JIAE

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The Ministry of Public Works and Public Housing (PUPR) conducted a traffic survey to determine the total number of vehicles and classify them according to the Bina Marga vehicle categorisation. The survey has thus far been carried out manually. As a result, surveys take a lot of time and money to perform. Additionally, as the survey scope grows, so will the requirement for surveyors. Therefore, a substitute that can execute the survey procedure automatically and with tolerable accuracy is required. One solution is to utilise deep learning technology to detect and categorise vehicles that can be used in apps. The program is designed as a web application that provides a summary of vehicle calculations and receives video data from traffic recordings. The deep learning model used is YOLOv4 which is trained to recognise vehicle classes following Bina Marga vehicle types. The model was trained and tested using the Python programming language and the Darknet framework on the Google Colab platform. The YOLOv4 and DeepSORT method with custom dataset reached a decent accuracy of 67.94%, considering the limited 1000 images used for training the model.

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Detection of Road Cracks Using Convolutional Neural Networks and Threshold Segmentation

Cited by 5 publications

References 26 publications

Learning a Multimodal 3D Face Embedding for Robust RGBD Face Recognition

Learning a Multimodal 3D Face Embedding for Robust RGBD Face Recognition

The effect of rising prices of subsidized fuel on the use of private cars

Counting Various Vehicles using YOLOv4 and DeepSORT

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