To address the occlusion problem of pedestrian detection algorithm when applied in traffic scenarios, this paper presents an occlusion-aware algorithm combined with dual attention mechanism for pedestrian detection. Based on the RetinaNet framework, the spatial-wise attention mechanism and channel-wise attention mechanism were utilized in regression and classification branches respectively, guiding the detector to pay more attention to the visible parts of pedestrians. Moreover, visible bounding box information of pedestrians was introduced to optimize the traditional regression loss function, so that it can adaptively adjust the weights of predicted boxes according to the degree of occlusion. Experiments on Caltech and CityPerson datasets show that the proposed algorithm had better robustness and higher accuracy than other eight advanced algorithms such as RetinaNet. Especially in the case of heavy occlusion, the log-average miss rate of the proposed algorithm was only 45.69%, which was 12% lower than those of other algorithms. Furthermore, the proposed algorithm could detect pedestrians in quasi-real-time. It processed 11.8 frames per second on Caltech dataset and 10.0 frames per second on CityPerson dataset. The detection methods of dual attention mechanism and occlusion-aware regression loss function proposed in this paper are feasible and effective, and have significant advantages for the processing of occluded pedestrians.