Abstract：This study focuses on the pedestrian spaces of campus streets at a university in southern China, utilizing Gradient-weighted Class Activation Mapping (Grad-CAM) to explore pedestrian visual perception experiences. Grad-CAM intuitively highlights key areas in street images that affect pedestrian comfort, automatically identifying and emphasizing visual elements such as vegetation and vehicles, and revealing their impact on the pedestrian experience and visual perception. By comparing Grad-CAM activation maps with eye-tracking data, and incorporating analyses from SHAP and kernel density estimation models, the study summarizes the main street characteristics that shape pedestrian perception, and uncovers the diverse effects of streetscape elements on visual perception. This study provides architects with an analytical method that is easy to understand and apply for the refined design of campus pedestrian spaces.
李韵琴，张嘉新*，谢雨辰. 基于Grad-CAM的校园街道步行空间视觉感知体验研究[J]. 新建筑, 2024, 42(6): 18-23.
LI Yunqin, ZHANG Jiaxin, XIE Yuchen. Visual Perception Experience of Campus Street Walking Space Based on Gradient-weighted Class Activation Mapping. New Architecture, 2024, 42(6): 18-23.
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