This paper proposes a neural network that fuses the data received from a camera system on a gantry to detect moving objects and calculate the relative position and velocity of the vehicles traveling on a freeway. This information is used to estimate the traffic flow. To estimate the traffic flows at both microscopic and macroscopic levels, this paper used YOLO v4 and DeepSORT for vehicle detection and tracking. The number of vehicles passing on the freeway was then calculated by drawing virtual lines and hot zones. The velocity of each vehicle was also recorded. The information can be passed to the traffic control center in order to monitor and control the traffic flows on freeways and analyze freeway conditions.

Detecting small objects in drone imagery remains challenging because of extreme object scale variations, dense scenes, and limited pixel information. Although recent YOLOv8 variants provide multiple model scales and architectural options, systematic guidance on their practical use in UAV-based detection remains limited. Accordingly, this study conducted a comprehensive empirical evaluation of the complete YOLOv8 family on the VisDrone dataset to assess the effects of the model capacity, input resolution, and architectural modifications on the small-object detection performance. The results showed that increasing the model capacity exhibited diminishing returns: YOLOv8l achieved the best overall accuracy (15.9% mAP50), while the larger YOLOv8x model exhibited a substantial performance degradation (7.32% mAP50) owing to training instability under data-constrained conditions. Scaling the input resolution from 640 to 1280 yielded a 25% improvement in the detection performance, substantially exceeding the gains obtained through architectural modifications, such as adding a P2 detection layer (+6%). The optimal configuration (YOLOv8l @ 1280) achieved a 488% improvement compared to the YOLOv5 baseline. These findings demonstrate that, for UAV-based small-object detection, prioritizing an appropriate model capacity and input resolution is more effective than increasing the architectural complexity.

Detecting small objects in drone imagery remains challenging because of extreme object scale variations, dense scenes, and limited pixel information. Although recent YOLOv8 variants provide multiple model scales and architectural options, systematic guidance on their practical use in UAV-based detection remains limited. Rather than proposing novel network architectures, this study provides a quantitative cost–benefit analysis and empirical deployment guidelines by comprehensively evaluating the complete YOLOv8 family on the VisDrone dataset to assess the effects of the model capacity, input resolution, and architectural modifications on the small-object detection performance. The results showed that increasing the model capacity exhibited diminishing returns: YOLOv8l achieved the best overall accuracy (15.9% mAP50), while the larger YOLOv8x model exhibited a substantial performance degradation (7.32% mAP50) owing to training instability under data-constrained conditions. Scaling the input resolution from 640 to 1280 yielded a 25% improvement in detection performance, substantially exceeding the gains obtained through architectural modifications, such as adding a P2 detection layer (+6%). The optimal configuration (YOLOv8l @ 1280) achieved a 488% improvement compared to the YOLOv5 baseline. These findings demonstrate that, for UAV-based small-object detection, prioritizing an appropriate model capacity and input resolution is more effective than increasing the architectural complexity.