Small and overlapping worker detection at construction sites

Park, Minsoo; Tran, Dai Quoc; Bak, JinYeong; Park, Seunghee

doi:10.1016/j.autcon.2023.104856

Cited by 32 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This integration led to a reduction in project managers' workload, indirectly contributing to a zero-accident occurrence throughout the project. While some results cited in this paper may not explicitly showcase the integration of applications in real projects, the data collected originates from actual construction sites and holds the potential for further development into tangible products [43], [84], [99].…”

Section: A Impact On Engineering and Constructionmentioning

confidence: 97%

“…Huang et al [98] achieved better construction worker hardhat wear detection performance by improving YOLO v3. Park et al [99] introduced DIoU and NMS for YOLO v5, utilizing weight triplet attention, expansion deature-level, and soft-pool to improve the performance and enhance the ability of detecting workers at construction sites, especially the ability to detect overlapping objects in complex environments was enhanced. Other studies have also involved various computer vision methods for algorithm improvements [100]- [106].…”

Section: B Computer Vision Algorithms' Innovationmentioning

confidence: 99%

“…On the other hand, Fang et al [56] and Son et al [62] both used the Faster R-CNN algorithm for object detection and achieved better performance. Park et al [99] proposed SOC-YOLO to improve the detection of construction workers in complex scenes when they overlap. Achieving more accurate localization for individual workers on a temporal scale requires combining various algorithms [112], [117].…”

Section: Figure 8 Analysis Of Research Countries or Areasmentioning

confidence: 99%

“…As algorithms mature, the focus on improving robustness becomes pertinent. For instance, Park et al [99] enhanced YOLO v5 with multiple strategies, enabling the detection of construction obscured and overlapped workers even in low light environments (Figure 10). Anticipating continued algorithmic and methodological advancements, coupled with ongoing dataset updates, the resolve of addressing the technique's robustness appears promising.…”

Section: B Technical Suitabilitymentioning

confidence: 99%

See 3 more Smart Citations

A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors

Li,

Miao,

Zou

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Construction workers' behaviors directly affects labor productivity and their own safety, thereby influencing project quality. Recognizing and monitoring the construction-related behaviors is therefore crucial for high-quality management and orderly construction site operation. Recent strides in computer vision technology suggest its potential to replace traditional manual supervision approaches. This paper explores research on monitoring construction workers' behaviors using computer vision. Through bibliometrics and content-based analysis, the authors present the latest research in this area from three perspectives: "Detection, Localization, and Tracking for Construction Workers," "Recognition of Workers' Construction Activities," and "Occupational Health and Safety Behavior Monitoring." In terms of the literature's volume, there has been a notable increase in this field. Notably, the focus on safety-related literature is predominant, underscoring the concern for occupational health. Vision algorithms have witnessed an increase in the utilization of object detection. The ongoing and future research trajectory is anticipated to involve multi-algorithm integration and an emphasis on enhancing robustness. Then the authors summarize the review from engineering impact and technical suitability, and analyze the limitations of current research from the perspectives of technical approaches and application scenarios. Finally, it discusses future research directions in this field together with generative AI models. Furthermore, the authors hope this paper can serves as a valuable reference for both scholars and engineers.

show abstract

Section: A Impact On Engineering and Constructionmentioning

confidence: 97%

Section: B Computer Vision Algorithms' Innovationmentioning

confidence: 99%

Section: Figure 8 Analysis Of Research Countries or Areasmentioning

confidence: 99%

Section: B Technical Suitabilitymentioning

confidence: 99%

See 2 more Smart Citations

A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors

Li,

Miao,

Zou

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The use of closed-circuit television (CCTV) cameras for safety monitoring on construction sites has played a crucial role in mitigating risks. Despite this, the full potential of CCTV data is often underutilized, primarily due to the majority of existing approaches employing single detection models without considering the full context of CCTV video inputs (Park et al, 2022(Park et al, , 2023Tran et al, 2020). In response to this issue, this study proposes a novel and robust system that incorporates a multimodal detection and depth map estimation algorithm, utilizing the power of deep learning.…”

Section: Introductionmentioning

confidence: 99%

Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data

Quoc Tran,

Jeon,

Son

et al. 2023

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Self Cite

View full text Add to dashboard Cite

The use of closed-circuit television (CCTV) for safety monitoring is crucial for reducing accidents in construction sites. However, the majority of currently proposed approaches utilize single detection models without considering the context of CCTV video inputs. In this study, a multimodal detection, and depth map estimation algorithm utilizing deep learning is proposed. In addition, the point cloud of the test site is acquired using a terrestrial laser scanning scanner, and the detected object's coordinates are projected into global coordinates using a homography matrix. Consequently, the effectiveness of the proposed monitoring system is enhanced by the visualization of the entire monitored scene. In addition, to validate our proposed method, a synthetic dataset of construction site accidents is simulated with Twinmotion. These scenarios are then evaluated with the proposed method to determine its precision and speed of inference. Lastly, the actual construction site, equipped with multiple CCTV cameras, is utilized for system deployment and visualization. As a result, the proposed method demonstrated its robustness in detecting potential hazards on a construction site, as well as its real-time detection speed

show abstract

Real-Time Detection of Construction Objects on Oversized Excavation Sites by Enhanced YOLO_v7 Network Using UAV-Captured Videos

Zhao,

Liao,

Yang

et al. 2024

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

Small and overlapping worker detection at construction sites

Cited by 32 publications

References 43 publications

A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors

A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors

Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data

Real-Time Detection of Construction Objects on Oversized Excavation Sites by Enhanced YOLO_v7 Network Using UAV-Captured Videos

Contact Info

Product

Resources

About