IEPet: A Lightweight Multiscale Infrared Environmental Perception Network

In recent years, the development of unmanned driving technology requires continuous progress in environment perception technology. Aiming at the key research direction of infrared environment perception in unmanned driving technology, a lightweight real-time detection network model for infrared environment perception, IEPet, is proposed. The model backbone adds the BottleneckCSP module and the proposed DCAP attention module, which can significantly improve the detection ability and spatial position perception ability while maintaining light weight. At the same time, the model improves the detection accuracy by using a 3-scales detection head. Comparative experiments on the unmanned driving data set show that compared with the lightweight model YOLOv4-tiny, the model proposed in this paper has an increase in F1 Score by 1.48% and an average detection accuracy by 6.37% to reach 84.31%. And the model is lighter. It shows that the proposed IEPet model can better meet the excellent performance required for infrared environment perception.