Depth Estimation From a Single RGB Image Using Fine-Tuned Generative Adversarial Network

Islam, Naeem Ul; Park, Jaebyung

doi:10.1109/access.2021.3060435

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…Dung et al [59] proposed a deep FCN for semantic segmentation on concrete images to detect cracks and determine their densities. VGG16 was used as a backbone for the FCN encoder due to its superior performance to ResNet and InceptionV3 in terms of classification of the crack images [83]. Five hundred annotated images from a publicly available concrete dataset were used to train the FCN encoder-decoder [59].…”

Section: Fully Convolutional Network (Fcn)mentioning

confidence: 99%

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

et al. 2023

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Artificial Intelligence (AI) and allied disruptive technologies have revolutionized the scientific world. However, civil engineering, in general, and infrastructure management, in particular, are lagging behind the technology adoption curves. Crack identification and assessment are important indicators to assess and evaluate the structural health of critical city infrastructures such as bridges. Historically, such critical infrastructure has been monitored through manual visual inspection. This process is costly, time-consuming, and prone to errors as it relies on the inspector’s knowledge and the gadgets’ precision. To save time and cost, automatic crack and damage detection in bridges and similar infrastructure is required to ensure its efficacy and reliability. However, an automated and reliable system does not exist, particularly in developing countries, presenting a gap targeted in this study. Accordingly, we proposed a two-phased deep learning-based framework for smart infrastructure management to assess the conditions of bridges in developing countries. In the first part of the study, we detected cracks in bridges using the dataset from Pakistan and the online-accessible SDNET2018 dataset. You only look once version 5 (YOLOv5) has been used to locate and classify cracks in the dataset images. To determine the main indicators (precision, recall, and mAP (0.5)), we applied each of the YOLOv5 s, m, and l models to the dataset using a ratio of 7:2:1 for training, validation, and testing, respectively. The mAP (Mean average precision) values of all the models were compared to evaluate their performance. The results show mAP values for the test set of the YOLOv5 s, m, and l as 97.8%, 99.3%, and 99.1%, respectively, indicating the superior performance of the YOLOv5 m model compared to the two counterparts. In the second portion of the study, segmentation of the crack is carried out using the U-Net model to acquire their exact pixels. Using the segmentation mask allocated to the attribute extractor, the pixel’s width, height, and area are measured and visualized on scatter plots and Boxplots to segregate different cracks. Furthermore, the segmentation part validated the output of the proposed YOLOv5 models. This study not only located and classified the cracks based on their severity level, but also segmented the crack pixels and measured their width, height, and area per pixel under different lighting conditions. It is one of the few studies targeting low-cost health assessment and damage detection in bridges of developing countries that otherwise struggle with regular maintenance and rehabilitation of such critical infrastructure. The proposed model can be used by local infrastructure monitoring and rehabilitation authorities for regular condition and health assessment of the bridges and similar infrastructure to move towards a smarter and automated damage assessment system.

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Section: Fully Convolutional Network (Fcn)mentioning

confidence: 99%

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

et al. 2023

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“…The idea of the GAN-based image-depth estimation method is that the generator generates a depth map and the discriminator determines whether the depth map is true or false. For example, in the method proposed by Islam [13] et al in 2021, for the input image and the depth map pair, the generator generates two fake depth maps, and then the discriminator determines which of the three depth maps is the true image pair with the input image. All the above methods are supervised learning methods.…”

Section: Image-depth Estimation Based On Deep Learningmentioning

confidence: 99%

Rendering Natural Bokeh Effects Based on Depth Estimation to Improve the Aesthetic Ability of Machine Vision

Wang

Zhang

et al. 2022

Machines

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Machine vision is the key to realizing computer-vision tasks such as human–computer interaction and autonomous driving. However, human perception of an image’s beauty is innate. If a machine can increase aesthetic awareness, it will greatly improve the comfort of human perception in human–computer interaction. The bokeh effect is one of the most important ways to improve the artistic beauty of photographic images and the image aesthetic quality. Bokeh rendering of an image can highlight the main object of the image and blur unnecessary or unattractive background details. The existing methods usually have unrealistic rendering effects with obvious artifacts around the foreground boundary. Therefore, we propose a natural bokeh-rendering method based on depth estimation that satisfies the following characteristics: objects in the focal plane are clear and out-of-focus objects are blurred; and the further away from the focal plane, the more blurred the objects are. Our method consists of three modules: depth estimation, background subdivision, and bokeh rendering. The background-subdivision module can select different focal planes to obtain different blur radii, making the bokeh-rendering effect more diverse, so that it does not oversegment objects. The bokeh-rendering module adjusts the degree of bokeh by adjusting the blur-radius factor. In the experimental section, we analyze the model results and present the visualization results.

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“…In the current decade, deep learning has revolutionized the artificial intelligence (AI) realm and continues to do so. Deep learning algorithms have shown remarkable performance practically, especially in computer vision [3][4][5][6]. In this context, machine vision approaches are a hot research area where robotic solutions are developed to automate the processes [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Context Aggregation for Strawberry Fruit Recognition and Disease Phenotyping

Ilyas¹,

Khan

Umraiz

et al. 2021

IEEE Access

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Timely harvesting and disease identification of strawberry fruits is a major concern for commercial level cultivators. Failing to harvest the grown strawberries can result in the fruit rotting which makes their damaged tissues more prone to grey mold pathogens. Immediate removal of the overgrown or diseased strawberries is inevitable to curb the mass spreading of the pathogen. In this paper, we propose a deep learning-based framework to identify three different strawberry fruit classes (unripe, partially ripe and ripe), as well as a class of overgrown or diseased strawberries. We equip the proposed convolutional encoderdecoder network with three different modules. One for adaptively controlling receptive filed size of the network to detect objects of multiple sizes. Second for controlling the flow of salient features (information) to the deeper layers of the network and the other for controlling the architecture's computational complexity. These modules combined, outperform the previous state-of-the-art semantic segmentation networks on the task of strawberry fruit phenotyping. We also introduce a dataset collected from different farms to evaluate the performance of the network. Quantitative and qualitative results show that notwithstanding heterogeneity in the data and the effect of the real-field variations, our approach produced remarkable results with a 3% increase in mean intersection over union as compared to the other state-of-the-art networks and was able to recognize diseased fruits with a precision of 92.45%.

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Depth Estimation From a Single RGB Image Using Fine-Tuned Generative Adversarial Network

Cited by 8 publications

References 35 publications

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

Rendering Natural Bokeh Effects Based on Depth Estimation to Improve the Aesthetic Ability of Machine Vision

Multi-Scale Context Aggregation for Strawberry Fruit Recognition and Disease Phenotyping

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