Multiscale and multitarget thermal infrared traffic detection based on improved YOLOv8

With the rapid advancement of object detection technology, its role in intelligent transportation systems has become increasingly significant. However, two major challenges persist in complex traffic environments: 1) the need for further optimization of real-time detection and 2) limited detection accuracy under multi-scale and multi-target conditions. To address these issues, we propose a Multi-Scale and Multi-Target Thermal Infrared Traffic Detection method based on an improved YOLOv8. A novel Parm_C2f module is introduced, which increases the number of parameters without compromising the inference speed, thereby enhancing the model's encoding capability. Additionally, the DNC_Detect Head, leveraging deformable convolution, is incorporated to improve the model's performance in multi-scale and multi-target detection. The experimental results demonstrate that the proposed method outperforms the YOLOv8 algorithm on challenging thermal infrared radiation datasets, showcasing its robust object detection performance in the transportation domain.