A Conceptual Framework for Inclusive Smart City Monitoring Using YOLOv10

Abstract

The shift towards digital economy has made it increasingly clear that the city digital governance needs to be adaptable enough to ensure that the city it serves is as inclusive and sustainable as possible. However, real time object detection powered by deep learning no longer is a straightforward technical problem of model replacement for computational improvements or inference speedups. It needs to become more scalable, contextually more correct, and fairer as the risks of AI-powered city monitoring to marginalize already underserved and underrepresented communities grows with scope of its deployment and range of its applications. To address this shortcoming, this paper attempts to provide a conceptual framework for responsible deployment of state-of-art YOLOv10 object detection models for situational awareness in the smart cities. Linking the strengths of YOLOv10 (multiscale object detection with edge-compatible architectures and improved contextual understanding) and the key principles of digital inclusion, transparency and effective governance, the proposed conceptual framework will help improve both scalability and the contextuality as well as fairness in deployment of AI-based object detection applications towards informed and responsible urban decision-making. A five-layered conceptual model is offered from data collection to ethical considerations and representative applications of inclusive city digital governance such as accessibility mapping, emergency response and smart waste management. Overall, this work attempts to situate the ongoing discussion of responsible AI in object detection on a firmer foundation with ethical design considerations by drawing meaningful parallels to the emerging field of inclusive urban digital communication.