Accurate traffic flow prediction is a crucial step in building an intelligent transportation system, and it is of great significance for alleviating urban traffic congestion and optimizing travel routes. Due to the complex spatial topology of the transportation network and the highly nonlinear temporal dynamic characteristics of the flow data, traditional prediction methods are difficult to fully capture the inherent spatio-temporal dependencies. Therefore, this paper proposes a traffic flow prediction model based on variant hybrid multi-hop graph convolution. Firstly, by introducing a multi-hop graph convolution operator, the model explicitly aggregates the spatial information of multiple-o... More >

In response to the challenges of insufficient accuracy in face detection and missed small targets under low-light conditions, this paper proposes a detection scheme that combines image preprocessing and detection model optimization. Firstly, Zero-DCE low-light enhancement is introduced to adaptively restore image details and contrast, providing high-quality inputs for subsequent detection. Secondly, YOLOv11n is enhanced through the following improvements: a P2 small-target detection layer is added while the P5 layer is removed, addressing the original model's deficiency in detecting small targets and streamlining the computational process to balance model complexity and efficiency; the P2 up... More >