Image Retraining Using TensorFlow Implementation of the Pretrained Inception-v3 Model for Evaluating Gravel Road Dust

Albatayneh, Omar; Forslof, Lars; Ksaibati, Khaled

doi:10.1061/(asce)is.1943-555x.0000545

Cited by 21 publications

(12 citation statements)

References 24 publications

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“…This study further compares the proposed dust detection approach with the reported findings in the literature (Albatayneh et al, 2020;Lei et al, 2021). The results listed in Table 4 indicate that the developed dust detection method could provide more detailed dust information regarding dust events' locations.…”

Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 65%

“…In contrast, the other two methods can only classify the dust images, which do not localize dust events in individual images. Although Albatayneh et al (2020) trained an inception-v3 model on 3,200 real-world images, the proposed method still outperforms previous methods with a 97.63% precision, an 88.03% recall and a 0.93 F1 score, even when training on the synthetic dataset. One of the most…”

Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 88%

“…Lei et al (2021) used an improved K-means clustering algorithm to identify construction dust images. Albatayneh et al (2020) developed an image classifier with an inception-v3 model to recognize gravel road dust and achieved a classification accuracy of 72%. However, the current vision-based studies for dust monitoring primarily focus on utilizing image classification algorithms that do not localize dust events in individual images.…”

Section: Air Quality Monitoring On Construction Sitesmentioning

confidence: 99%

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Machine learning using synthetic images for detecting dust emissions on construction sites

Xiong

Tang

2021

SASBE

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PurposeAutomated dust monitoring in workplaces helps provide timely alerts to over-exposed workers and effective mitigation measures for proactive dust control. However, the cluttered nature of construction sites poses a practical challenge to obtain enough high-quality images in the real world. The study aims to establish a framework that overcomes the challenges of lacking sufficient imagery data (“data-hungry problem”) for training computer vision algorithms to monitor construction dust.Design/methodology/approachThis study develops a synthetic image generation method that incorporates virtual environments of construction dust for producing training samples. Three state-of-the-art object detection algorithms, including Faster-RCNN, you only look once (YOLO) and single shot detection (SSD), are trained using solely synthetic images. Finally, this research provides a comparative analysis of object detection algorithms for real-world dust monitoring regarding the accuracy and computational efficiency.FindingsThis study creates a construction dust emission (CDE) dataset consisting of 3,860 synthetic dust images as the training dataset and 1,015 real-world images as the testing dataset. The YOLO-v3 model achieves the best performance with a 0.93 F1 score and 31.44 fps among all three object detection models. The experimental results indicate that training dust detection algorithms with only synthetic images can achieve acceptable performance on real-world images.Originality/valueThis study provides insights into two questions: (1) how synthetic images could help train dust detection models to overcome data-hungry problems and (2) how well state-of-the-art deep learning algorithms can detect nonrigid construction dust.

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Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 65%

Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 88%

Section: Air Quality Monitoring On Construction Sitesmentioning

confidence: 99%

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Machine learning using synthetic images for detecting dust emissions on construction sites

Xiong

Tang

2021

SASBE

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“…The proposed DenseNet-II model finds its basis on some deep learning models like DenseNet (Sun et al 2020 ), VGG-16 (Mateen et al 2019 ), InceptionV3 (Albatayneh et al 2020 ) and ResNet (Khan et al 2019 ). It extracts the main features from every algorithm and amalgamates them together to form a robust classifier.…”

Section: The Proposed Model: Densenet-iimentioning

confidence: 99%

DenseNet-II: an improved deep convolutional neural network for melanoma cancer detection

2022

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Research in the field of medicine and relevant studies evince that melanoma is one of the deadliest cancers. It defines precisely that the condition develops due to uncontrolled growth of melanocytic cells. The current trends in any disease detection revolve around the usage of two main categories of models; these are general machine learning models and deep learning models. Further, the experimental analysis of melanoma has an additional requirement of visual records like dermatological scans or normal camera lens images. This further accentuates the need for a more accurate model for melanoma detection. In this work, we aim to achieve the same, primarily by the extensive usage of neural networks. Our objective is to propose a deep learning CNN framework-based model to improve the accuracy of melanoma detection by customizing the number of layers in the network architecture, activation functions applied, and the dimension of the input array. Models like Resnet, DenseNet, Inception, and VGG have proved to yield appreciable accuracy in melanoma detection. However, in most cases, the dataset was classified into malignant or benign classes only. The dataset used in our research provides seven lesions; these are melanocytic nevi, melanoma, benign keratosis, basal cell carcinoma, actinic keratoses, vascular lesions, and dermatofibroma . Thus, through the HAM10000 dataset and various deep learning models, we diversified the precision factors as well as input qualities. The obtained results are highly propitious and establish its credibility.

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“…All codes are implemented using Python under the TensorFlow framework. TensorFlow is a Google open-source software that is widely used in DL algorithms [70,71]. The main compiler was PyCharm5.0.3, which was used for building the S-CNN and V-CNN models and doing the GUI production.…”

Section: Production Of the Debris Flow Susceptibility Mapsmentioning

confidence: 99%

Spatial Predictions of Debris Flow Susceptibility Mapping Using Convolutional Neural Networks in Jilin Province, China

Chen

Qin

Qiao

et al. 2020

Water

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Debris flows are a major geological disaster that can seriously threaten human life and physical infrastructures. The main contribution of this paper is the establishment of two–dimensional convolutional neural networks (2D–CNN) models by using SAME padding (S–CNN) and VALID padding (V–CNN) and comparing them with support vector machine (SVM) and artificial neural network (ANN) models, respectively, to predict the spatial probability of debris flows in Jilin Province, China. First, the dataset is randomly divided into a training set (70%) and a validation set (30%), and thirteen influencing factors are selected to build the models. Then, multicollinearity analysis and gain ratio methods are used to quantify the predictive ability of factors. Finally, the area under the receiver operatic characteristic curve (AUC) and statistical methods are utilized to measure the accuracy of the models. The results show that the S–CNN model gets the highest AUC value of 0.901 in the validation set, followed by the SVM model, the V–CNN model, and the ANN model. Three statistical methods also show that the S–CNN model produces minimum errors compared with other models. The S–CNN model is hailed as an important means to improve the accuracy of debris–flow susceptibility mapping and provides a reasonable scientific basis for critical decisions.

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Image Retraining Using TensorFlow Implementation of the Pretrained Inception-v3 Model for Evaluating Gravel Road Dust

Cited by 21 publications

References 24 publications

Machine learning using synthetic images for detecting dust emissions on construction sites

Machine learning using synthetic images for detecting dust emissions on construction sites

DenseNet-II: an improved deep convolutional neural network for melanoma cancer detection

Spatial Predictions of Debris Flow Susceptibility Mapping Using Convolutional Neural Networks in Jilin Province, China

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