Remote proximity monitoring between mobile construction resources using camera-mounted UAVs

Kim, Daeho; Liu, Meiyin; Lee, Sang Hyun; Kamat, Vineet R.

doi:10.1016/j.autcon.2018.12.014

Cited by 180 publications

(64 citation statements)

References 30 publications

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“…In livestock censuses, an in-depth study was carried out on a large mammal census in an African savanna wildlife reserve based on UAV images and deep learning [22]. In addition, in the continuous safety monitoring and maintenance of infrastructure, a commercial UAV with a digital camera and convolutional neural networks was used to identify cracks in an aging concrete bridge [23]. These studies demonstrate that deep learning has good capabilities in processing UAV remote-sensing images.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

et al. 2020

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Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

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Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

et al. 2020

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“…Several CPP algorithms have been proposed for covering a regular or irregular shaped area of interest (AOI) with visual sensor and thermal sensor, etc. mounted on the UAV [12][13][14]. The basic approach adopted by most of the offline CPP algorithm is the area decomposition into non-overlapping subregions, determining the visiting sequence of the subregions, and covering decomposed regions individually in a back and forth manner to obtain a complete coverage path.…”

Section: Introductionmentioning

confidence: 99%

A New Coverage Flight Path Planning Algorithm Based on Footprint Sweep Fitting for Unmanned Aerial Vehicle Navigation in Urban Environments

Majeed

Lee

2019

Applied Sciences

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This paper presents a new coverage flight path planning algorithm that finds collision-free, minimum length and flyable paths for unmanned aerial vehicle (UAV) navigation in three-dimensional (3D) urban environments with fixed obstacles for coverage missions. The proposed algorithm significantly reduces computational time, number of turns, and path overlapping while finding a path that passes over all reachable points of an area or volume of interest by using sensor footprints’ sweeps fitting and a sparse waypoint graph in the pathfinding process. We devise a novel footprints’ sweep fitting method considering UAV sensor footprint as coverage unit in the free spaces to achieve maximal coverage with fewer and longer footprints’ sweeps. After footprints’ sweeps fitting, the proposed algorithm determines the visiting sequence of footprints’ sweeps by formulating it as travelling salesman problem (TSP), and ant colony optimization (ACO) algorithm is employed to solve the TSP. Furthermore, we generate a sparse waypoint graph by connecting footprints’ sweeps’ endpoints to obtain a complete coverage flight path. The simulation results obtained from various scenarios fortify the effectiveness of the proposed algorithm and verify the aforementioned claims.

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“…In construction, mainly due to unstructured and limited workspaces, unanticipated struck-by hazards involving mobile vehicle or equipment often arise, contributing to the significant number of construction fatalities [1]. According to The Center for Construction Research and Training, United States, from 2011 to 2015, total 925 struck-by fatalities were reported from construction [2].…”

Section: Introductionmentioning

confidence: 99%

“…A major research area for this issue is attuned to automating object localization, proximity monitoring, and accordingly struck-by hazard detection. Prior research leveraged various technologies-such as wireless sensors [5][6][7][8][9] and computer vision methods [1,[10][11]-and made a great progress on automation of struck-by hazard detection. It is expected that the successful deployment of such technologies will allow for prompt feedback to involved workers, thereby reducing the chance of an impending collision [1,5,10].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Prediction of Mobile Construction Resources Toward Pro-active Struck-by Hazard Detection

Kim¹,

Liu²,

Lee³

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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In construction, unanticipated struck-by hazards often arise, which have resulted in a significant number of construction fatalities. To address this problem, many studies have attempted to automate proximity monitoring and struck-by hazard detection using various technologies, such as wireless sensors and computer vision methods. While this technology focuses on understanding what is happening as hazards arise, it is not equipped to detect future hazards. In impending situations, detecting current hazards may not provide enough time for workers to take evasive actions. To address this challenge this study develops a trajectory prediction model for mobile construction resources. Specifically, this study conducts hyper-parameter tuning of a deep neural network, called Social Generative Adversarial Network to develop a prediction model capable of predicting more than five seconds. Further, a test on a real construction operations data follows to validate developed models' trajectory prediction accuracy. As a result, a developed model could achieve promising accuracy: the average displacement error and the final displacement error were 0.78 and 1.27 meters, respectively. The trajectory prediction allows for detecting future hazards, which will support proactive intervention in hazardous situations. It will ultimately contribute to promoting a safer working environment for construction workers.

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Remote proximity monitoring between mobile construction resources using camera-mounted UAVs

Cited by 180 publications

References 30 publications

Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

A New Coverage Flight Path Planning Algorithm Based on Footprint Sweep Fitting for Unmanned Aerial Vehicle Navigation in Urban Environments

Trajectory Prediction of Mobile Construction Resources Toward Pro-active Struck-by Hazard Detection

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