Temporal Visual Patterns of Construction Hazard Recognition Strategies.

computer vision construction hazard recognition (CHR) construction safety temporal qualitative comparative analysis (TQCA) visual patterns

Journal

International journal of environmental research and public health
ISSN: 1660-4601
Titre abrégé: Int J Environ Res Public Health
Pays: Switzerland
ID NLM: 101238455

Informations de publication

Date de publication:
20 08 2021
Historique:
received: 28 06 2021
revised: 12 08 2021
accepted: 17 08 2021
entrez: 27 8 2021
pubmed: 28 8 2021
medline: 1 9 2021
Statut: epublish

Résumé

Visual cognitive strategies in construction hazard recognition (CHR) signifies prominent value for the development of CHR computer vision techniques and safety training. Nonetheless, most studies are based on either sparse fixations or cross-sectional (accumulative) statistics, which lack consideration of temporality and yielding limited visual pattern information. This research aims to investigate the temporal visual search patterns for CHR and the cognitive strategies they imply. An experimental study was designed to simulate CHR and document participants' visual behavior. Temporal qualitative comparative analysis (TQCA) was applied to analyze the CHR visual sequences. The results were triangulated based on post-event interviews and show that: (1) In the potential electrical contact hazards, the intersection of the energy-releasing source and wire that reflected their interaction is the cognitively driven visual area that participants tend to prioritize; (2) in the PPE-related hazards, two different visual strategies, i.e., "scene-related" and "norm-guided", can usually be generalized according to the participants' visual cognitive logic, corresponding to the bottom-up (experience oriented) and top-down (safety knowledge oriented) cognitive models. This paper extended recognition-by-components (RBC) model and gestalt model as well as providing feasible practical guide for safety trainings and theoretical foundations of computer vision techniques for CHR.

Identifiants

pubmed: 34444528
pii: ijerph18168779
doi: 10.3390/ijerph18168779
pmc: PMC8391669
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

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Auteurs

Rui Cheng (R)

Department of Construction Management, Tsinghua University, Beijing 100084, China.

Jiaming Wang (J)

School of Economics and Management, Tongji University, Shanghai 200092, China.

Pin-Chao Liao (PC)

Department of Construction Management, Tsinghua University, Beijing 100084, China.

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Classifications MeSH