Full-Time Infrared Feature Pedestrian Detection Based on CSP Network

Due to the big variance between infrared images in day and night, the detection of infrared pedestrians in full-time has been a difficult point and has great significance both in theory and practice. Based on the deep learning method, we proposed three full-time infrared pedestrian detection models based on CSP (Center and Scale Prediction) network, namely, night model, daytime model, full-time model. Then, the performance of the three models are analyzed and compared quantitatively, and the results show that the performance of the full time model is the optimal one, the detection accuracy rate in day and night reach 83.86% and 80.22% respectively. Compared with the state-of-the-art method, the miss rate in day and night detection of the full time model in this paper is 12.23% and 4.56% lower respectively, thus the performance of the full-time model in our work is more effective.