## 6 Conclusions  
The accelerating global aging phenomenon, particularly evident in populous nations like China, has profoundly transformed multiple societal domains. Tourist attractions, serving as vital leisure spaces for older adults, now face pressing demands for age-adapted facility upgrades. Current assessment methodologies for age-friendliness remain constrained by conventional questionnaire surveys and observational techniques, approaches that suffer from inefficiency and substantial subjectivity, consequently yielding imprecise evaluations. To overcome these limitations, this investigation has introduced an innovative computer-vision-based assessment framework. The proposed system integrates YOLOv8 and MiVOLO deep learning models to automatically analyze visitor demographics (with specific age-group identification) and facility utilization patterns, thereby enabling the objective and efficient evaluation of age-friendly rest facility adequacy in tourism settings. The results of this study demonstrate the substantial advantages of computer-vision-based evaluation methods in enhancing assessment efficiency and accuracy. Through experimental research conducted at the Xuanwu Lake Scenic Area in Nanjing, this study verified that the method can accurately and in real-time detect the usage of rest areas by elderly visitors while also quantifying the age-friendly adequacy of rest facilities. This study concluded that the rest areas located more than 1 km from the entrance of the scenic spot had a facility density of fewer than 4 per 100 m, which was insufficient to fully accommodate the resting needs of elderly visitors, resulting in a low aging adequacy rate. Furthermore, there was significant potential for improvement in the quantity, distribution, and utilization of the scenic spot's open space service facilities. This study systematically validated the research findings through multi-period experiments and online review data analysis. The four-day repeated monitoring at FangQiao rest area demonstrated that the average facility occupancy rate (65.83%) and non-elderly user proportion at peak times (94.71%) closely aligned with single-day experimental data, confirming this area's low aging-friendly facility adequacy while verifying both methodological stability and sampling day representativeness. The cross-validation with online reviews revealed significant spatial differentiation in visitor satisfaction regarding rest facilities. Negative reviews about lakeside areas (FangQiao and LingQiao) corresponded with observed insufficient facility adequacy whereas positive feedback from non-lakeside areas (e.g., Ginkgo Boulevard with 10.5 facilities per 100 m) matched its higher adequacy rating. This multi-temporal monitoring and multi-source data verification approach significantly enhanced conclusion reliability, providing comprehensive evidence for scenic area facility planning. This paper proposes a resource allocation optimization scheme for open space areas with low aging adequacy. We use GIS spatial analysis methods to increase the number of open space facilities through buffer zone analysis. Through the above optimization measures, the scenic spot can effectively increase the aging-friendly adequacy rate of service facilities and improve the experience of the elderly tourists. This study introduced computer vision technology to quantify the spatiotemporal occupancy rate of rest facilities in scenic areas and the proportion of elderly users, proposing an efficient and objective aging-friendly evaluation method. Compared with traditional methods such as questionnaires and manual observations, the new approach addresses certain shortcomings, including high labor and time costs, cumbersome procedures, low efficiency, and strong subjectivity and randomness. It achieves the automated analysis of facility usage in scenic areas, providing quantifiable and objective indicators for aging-friendly evaluations, thereby filling the gap in technology-driven assessment tools in existing research.