The Internet of Things Drives Smart City Management: Enhancing Urban Infrastructure Efficiency and Sustainability

Abstract

In the context of current smart city development, the efficiency of urban management has become crucial. Target detection technology plays a vital role in addressing the complexity of urban environments. The authors propose a new method called YOLOv8_k, employing transfer learning as its foundation. This method leverages pre-trained model parameters from related tasks to incorporate prior knowledge into the target detection model, adapting better to the complexity of smart city management scenarios. Experimental results demonstrate the outstanding performance of YOLOv8_k. In specific experimental results, YOLOv8_k shows significant improvements across multiple evaluation metrics. The average precision in target detection tasks experiences a notable increase. Furthermore, in large-scale urban datasets, compared to traditional methods, YOLOv8_k exhibits higher responsiveness in handling large volumes of real-time data, further demonstrating its superiority in practical applications.