Thermal infrared radiation traffic detection based on improved YOLOv8 detector

Object detection plays a crucial role in intelligent transportation systems. However, the presence of numerous uncertainties in object detection within real traffic scenes poses significant challenges to achieving accurate and real-time detection. To address these challenges, this paper introduces a thermal radiation traffic detection algorithm based on an enhanced version of YOLOv8. Specifically, a novel DCC2F module is proposed, which not only enlarges the effective receptive field of the convolution kernel but also reduces computational complexity. Additionally, the introduction of the MBConv module enhances the feature extraction and detection capabilities of the YOLOv8 model. Experimental results demonstrate that the proposed method surpasses several other prominent algorithms when applied to challenging thermal infrared radiation datasets, showcasing its robust object detection capabilities.