Department of Construction Management, Tsinghua University, Beijing 100084, China.
School of Economics and Management, Tongji University, Shanghai 200092, China.
Int J Environ Res Public Health. 2021 Aug 20;18(16):8779. doi: 10.3390/ijerph18168779.
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.
视觉认知策略在施工危险识别(CHR)中具有重要的价值,对于 CHR 计算机视觉技术和安全培训的发展具有重要意义。然而,大多数研究都基于稀疏的注视点或横截面(累积)统计数据,缺乏对时间性的考虑,从而导致视觉模式信息有限。本研究旨在探讨 CHR 的时间视觉搜索模式及其所隐含的认知策略。设计了一项实验研究来模拟 CHR 并记录参与者的视觉行为。采用时间定性比较分析(TQCA)对 CHR 视觉序列进行分析。研究结果基于事后访谈进行三角验证,表明:(1)在潜在的电气接触危险中,能量释放源与反映其相互作用的电线的交点是参与者倾向于优先关注的认知驱动的视觉区域;(2)在与个人防护装备相关的危险中,根据参与者的视觉认知逻辑,通常可以概括出两种不同的视觉策略,即“场景相关”和“规范引导”,分别对应于自下而上(经验导向)和自上而下(安全知识导向)的认知模型。本文扩展了基于成分的识别(RBC)模型和格式塔模型,并为安全培训提供了可行的实践指导以及 CHR 计算机视觉技术的理论基础。