Excavator 3D pose estimation using deep learning and hybrid datasets

Assadzadeh, Amin; Arashpour, Mehrdad; Li, Heng; Hosseini, Reza; Elghaish, Faris; Baduge, Shanaka Kristombu

doi:10.1016/j.aei.2023.101875

Cited by 25 publications

(2 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, Liu et al [21] examined a computationally effective tracking approach from a 3D human skeleton extracted from stereo videos to observe the situation awareness of individuals to prevent accidents in construction. Assadzadeh et al used YOLOv5 to detect the excavator and HRNet to perform a 2D pose estimation within the predicted bounding box [65]. Wen et al utilized a modified Keypoint R-CNN algorithm to extract the 2D pose of an excavator from video frames [66].…”

Section: Pose Estimationmentioning

confidence: 99%

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

Khan,

Zaidi,

Yang

et al. 2023

Buildings

View full text Add to dashboard Cite

Noncompliance with safety rules is a major cause of unsatisfactory performance in construction safety worldwide. Although some research efforts have focused on using computer vision (CV) methods for safety rule inspection, these methods are still in their early stages and cannot be effectively applied on construction job sites. Therefore, it is necessary to present a feasible prototype and conduct a detailed analysis of safety rules to ensure compliance at the job site. This study aims to extend the validation of safety rule analysis through four case scenarios. The proposed structured classification of safety rules includes categorizing them based on project phases and work stages. The construction phase-related rules are divided into four groups: (1) before work, (2) with intervals, (3) during work, and (4) after work. To validate the proposed framework, this research developed prototypes for each group’s scenarios using deep learning algorithms, a storage database to record compliance with safety rules, and an Android application for edge computing, which is required in the “before work” and “after work” groups. The findings of this study could contribute to the development of a compact CV-based safety monitoring system to enhance the current safety management process in the construction industry.

show abstract

Section: Pose Estimationmentioning

confidence: 99%

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

Khan,

Zaidi,

Yang

et al. 2023

Buildings

View full text Add to dashboard Cite

show abstract

“…[1][2][3][4][5][6]. For the special environment of on-orbit work, the pose estimation [7,8] algorithm based on a lowquality, low-power monocular sensor provides a feasible scheme for space application, and it has received extensive attention from scientific research institutions and researchers. Some institutions [9][10][11][12] have carried out relevant studies and semi-physical simulation experiments on the pose estimation of non-cooperative space objects by using monocular vision cameras.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer

Ye,

Ren,

Zhu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Non-cooperative space object pose estimation is a key technique for spatial on-orbit servicing, where pose estimation algorithms based on low-quality, low-power monocular sensors provide a practical solution for spaceborne applications. The current pose estimation methods for non-cooperative space objects using monocular vision generally consist of three stages: object detection, landmark regression, and perspective-n-point (PnP) solver. However, there are drawbacks, such as low detection efficiency and the need for prior knowledge. To solve the above problems, an end-to-end non-cooperative space object pose estimation learning algorithm based on dual-channel transformer is proposed, a feature extraction backbone network based on EfficientNet is established, and two pose estimation subnetworks based on transformer are also established. A quaternion SoftMax-like activation function is designed to improve the precision of orientation error estimating. The method only uses RGB images, eliminating the need for a CAD model of the satellite, and simplifying the detection process by using an end-to-end network to directly detect satellite pose information. Experiments are carried out on the SPEED dataset provided by the European Space Agency (ESA). The results show that the proposed algorithm can successfully predict the satellite pose information and effectively decouple the spatial translation information and orientation information, which significantly improves the recognition efficiency compared with other methods.

show abstract

Accurate Detection of the Workers and Machinery in Construction Sites Considering the Occlusions

Wang,

Liu,

Peng

et al. 2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

Excavator 3D pose estimation using deep learning and hybrid datasets

Cited by 25 publications

References 73 publications

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer

Accurate Detection of the Workers and Machinery in Construction Sites Considering the Occlusions

Contact Info

Product

Resources

About