DA-Net: Pedestrian Detection Using Dense Connected Block and Attention Modules

Abstract

Pedestrian detection plays an important role in some areas such as autonomous driving, but due to heavy occlusion and various scales, it is still challenging. In this article, we propose an improved pedestrian detection method called DA-Net based on the two-stage detector Feature Pyramid Network (FPN). DA-Net adds Dense Connected Block (DCB), a combination of channel-wise attention module (CWAM) and global attention module (GAM) to the network. FPN can produce features with various scales and semantic information, which is good for the detection of pedestrians on various scales. Due to many small-scale targets in pedestrian detection, we only regard the low layers with enough details of targets in FPN as prediction layers. After several DCBs to deepen the network, prediction layers in our network can encode richer semantic information of targets, which can make the location of a target more precisely. In order to highlight visible parts of occluded pedestrians and ignore occluded parts, CWAM weights each channel of features with different importance. GAM aggregates global information and long-range dependencies for small-scale and occluded targets. Thus, the combination of CWAM and GAM is not only beneficial for coping with occlusion problem in pedestrian detection, but also for gaining environmental information for small-scale targets. Evaluation results on CUHK and CityPersons datasets show that our proposed method achieves improved performance with log-average miss rate reduction of 9.6% on the CUHK dataset and 6.1% on the Heavy subset of CityPersons dataset compared with FPN.