YOLO-Fusion and Internet of Things: Advancing object detection in smart transportation
Jun Tang, Caixian Ye, Xianlai Zhou, Lijun Xu
Abstract
In intelligent transportation systems , traditional object detection algorithms struggle to handle complex environments and varying lighting conditions , particularly when detecting small targets and processing multimodal data. Furthermore, existing IoT frameworks are limited in their efficiency for real-time data collection and processing, leading to data transmission delays and increased resource consumption, which constrains the overall performance of intelligent transportation systems . To address these issues, this paper proposes a novel deep learning model, YOLO-Fusion. Based on the YOLOv8 architecture, this model innovatively integrates infrared and visible-light images, utilizing FusionAttention and Dynamic Fusion modules to optimize the fusion of multimodal information . To further enhance detection performance, this paper designs a Fusion-Dynamic Loss, improving the model’s performance in complex intelligent transportation scenarios. To support the efficient operation of YOLO-Fusion, this paper also introduces an IoT framework that uses intelligent sensors and edge computing technology to achieve real-time collection, transmission and processing of traffic data, significantly improving data timeliness and accuracy. Experimental results demonstrate that YOLO-Fusion significantly outperforms traditional methods on the DroneVehicle and FLIR datasets, showcasing its broad application potential in intelligent traffic monitoring and management.