An Image Augmentation Method for Detecting Construction Resources Using Convolutional Neural Network and UAV Images

Bang, Su Sik; Baek, Francis; Park, Somin; Kim, Wontae; Kim, Hyoungkwan

doi:10.22260/isarc2019/0085

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Additionally, this study offers valuable information about research themes published on the topic of deep learning and its implementation in the construction industry through cluster analysis and critical review. Despite the contributions offered, the (Bang et al 2019, Chen et al 2020, Guo et al 2020, Slaton et al 2020, Xuehui et al 2021, Zhou et al 2021b, Lin et al 2021, Sim et al 2021 (Olanrewaju et al 2020, Kamal et al 2020, Liu et al 2020, Scarpiniti et al 2021)…”

Section: Discussionmentioning

confidence: 99%

Scientometric analysis and critical review on the application of deep learning in the construction industry

et al. 2023

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Deep learning in construction industry has attracted increasing attention among researchers. In this article, scientometric analysis and critical review are performed to analyze the state-of-the-art literature on the application of deep learning in construction. This research used the science mapping method to quantitatively and systematically analyze 423 related bibliographic records retrieved from the Scopus database, and further, a critical review is performed on collected themes. The results of the critical review indicate that deep convolution neural networks, you only look once, residual neural networks and fast region-based convolution neural networks have been the most widely used methods in the construction industry. The most commonly addressed problems in the construction industry using deep learning methods includes classification of construction equipment, worker's safety helmet detection and ergonomics analysis. This paper provides an in-depth understanding of the existing literature along with the challenges and future direction of deep learning in the construction industry.

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Section: Discussionmentioning

confidence: 99%

Scientometric analysis and critical review on the application of deep learning in the construction industry

et al. 2023

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“…Also, Son points out that the visibility of the equipment operator is inherently poor [51], which is consistent with our point of view. Recently, Bang proposed an image augmentation method to enhance the performance of objects detector on construction sites, achieving a recall of 66.76% and precision of 53.08% experimentally on the UAV-based resources [52].…”

Section: The Previous Contributions On Detecting Mobile Machinesmentioning

confidence: 99%

KIT MOMA: A Mobile Machines Dataset

Xiang,

Wang,

et al. 2020

Preprint

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Mobile machines typically working in a closed site, have a high potential to utilize autonomous driving technology. However, vigorously thriving development and innovation are happening mostly in the area of passenger cars. In contrast, although there are also many research pieces about autonomous driving or working in mobile machines, a consensus about the SOTA solution is still not achieved. We believe that the most urgent problem that should be solved is the absence of a public and challenging visual dataset, which makes the results from different researches comparable. To address the problem, we publish the KIT MOMA dataset, including eight classes of commonly used mobile machines, which can be used as a benchmark to evaluate the SOTA algorithms to detect mobile construction machines. The view of the gathered images is outside of the mobile machines since we believe fixed cameras on the ground are more suitable if all the interesting machines are working in a closed site. Most of the images in KIT MOMA are in a real scene, whereas some of the images are from the official website of top construction machine companies. Also, we have evaluated the performance of YOLO v3 on our dataset, indicating that the SOTA computer vision algorithms already show an excellent performance for detecting the mobile machines in a specific working site. Together with the dataset, we also upload the trained weights, which can be directly used by engineers from the construction machine industry. The dataset, trained weights, and updates can be found on our Github. Moreover, the demo can be found on our Youtube.

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Where Am I? SLAM for Mobile Machines on a Smart Working Site

Xiang

Dianzhao

et al. 2022

Vehicles

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The current optimization approaches of construction machinery are mainly based on internal sensors. However, the decision of a reasonable strategy is not only determined by its intrinsic signals, but also very strongly by environmental information, especially the terrain. Due to the dynamic changing of the construction site and the consequent absence of a high definition map, the Simultaneous Localization and Mapping (SLAM) offering the terrain information for construction machines is still challenging. Current SLAM technologies proposed for mobile machines are strongly dependent on costly or computationally expensive sensors, such as RTK GPS and cameras, so that commercial use is rare. In this study, we proposed an affordable SLAM method to create a multi-layer grid map for the construction site so that the machine can have the environmental information and be optimized accordingly. Concretely, after the machine passes by the grid, we can obtain the local information and record it. Combining with positioning technology, we then create a map of the interesting places of the construction site. As a result of our research gathered from Gazebo, we showed that a suitable layout is the combination of one IMU and two differential GPS antennas using the unscented Kalman filter, which keeps the average distance error lower than 2m and the mapping error lower than 1.3% in the harsh environment. As an outlook, our SLAM technology provides the cornerstone to activate many efficiency improvement approaches.

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An Image Augmentation Method for Detecting Construction Resources Using Convolutional Neural Network and UAV Images

Cited by 3 publications

References 11 publications

Scientometric analysis and critical review on the application of deep learning in the construction industry

Scientometric analysis and critical review on the application of deep learning in the construction industry

KIT MOMA: A Mobile Machines Dataset

Where Am I? SLAM for Mobile Machines on a Smart Working Site

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