Pedestrian detection is crucial for various applications, including intelligent transportation and video surveillance systems. Although recent research has advanced pedestrian detection models like the YOLO series, they still face limitations in handling diverse pedestrian scales, leading to performance challenges. To address these issues, we propose HF-YOLO, an advanced pedestrian detection model. HF-YOLO tackles the complexities of pedestrian detection in complex scenes by addressing scale variations and occlusions among pedestrians. In the feature fusion stage, our algorithm leverages both shallow localization information and deep semantic information. This involves fusing P2 layer features and adding a high-resolution detection layer, significantly improving the detection of small-scale pedestrians and occluded instances. To enhance feature representation, HF-YOLO incorporates the HardSwish activation function, introducing more non-linear factors and strengthening the model’s ability to represent complex and discriminative features. Additionally, to address regression imbalance, a balance factor is introduced to the CIoU loss function. This modification effectively resolves the imbalance problem and enhances pedestrian localization accuracy. Experimental results demonstrate the effectiveness of our proposed algorithm. HF-YOLO achieves notable improvements, including a 3.52% increase in average precision, a 1.35% boost in accuracy, and a 4.83% enhancement in recall. Moreover, the algorithm maintains real-time performance with a detection time of 8.5ms, meeting the stringent requirements of real-time applications.

Urban livability is an important factor affecting the sustainable development of modern cities. Safe livability is an important part of urban livability. In view of this, this paper takes security as an example, and based on the actual data of Futian District, Shenzhen City, China, establishes a multi-agent simulation model of urban safe livability. The dynamic interaction feedback mechanism between decision-making behaviors of residents and urban safe livability under the influence of environment and policies has been explored, and residents’ decision-making simulation of the change of urban safe livability is realized. Finally, the main factors influencing urban safe livability are summarized through simulation conclusions. The research can not only provide scientific suggestions for improving the safe livability of Shenzhen, it also provides strong support for the sustainable development of the city.

The integration of generative AI into programming education has produced a widely reported tension between performance and learning. We distinguish immedia

Improving the urban livability status has become the core goal of urban development, and reasonable assessment of the urban livability status and impact is crucial. By combining an objective environment with residents’ subjective cognition, an artificial society (urban livability change artificial society; ULC-AS) is constructed. The ULC-AS includes four types of agents, namely, government, family, resident and safety facility management agency agents, and recognizes dynamic interaction among various agents and between agents and the environment. Taking the Futian District of Shenzhen as an example, this paper examines factors such as migrants, birth policies, and government investment. We simulate the interactions among resident satisfaction changes, relocation decision-making behavior and urban safety livability and analyze the change processes and development trends of urban safety livability under multiple scenarios. Our main result indicates that population change and investment construction are important factors affecting urban safety livability. At present, the population of the Futian District is saturated. Therefore, the government must assess the urban safety livability and increase investment in high-demand areas. Through this method, the goals of urban resource allocation optimization and coordinated urban development can be achieved.

Urban livability is an important factor in decisions regarding modern urban construction. Based on multi-agent modeling theory, a model of the changes in urban safety livability is proposed to analyze the present situation and the trends in urban safety livability in complex urban systems. Considering safety as an example, five environmental indicators, namely public security, fire protection, traffic, emergency response, and residence, are selected. The interactive dynamic feedback mechanisms among resident decision-making behaviors and urban safety livability in the context of these five indicators and relevant policies are provided. A simulation of the changes in urban safety livability is implemented based on resident behaviors. The results show that the multi-agent modeling method is effective for analyzing urban safety livability and can provide decision support for urban development planning.