Pedestrian detection using multi-scale squeeze-and-excitation module

Abstract

Computer vision systems are major research items for autonomous vehicles. However, it is often challenging to understand the road scene, especially when objects are small and overlapping. To address these problems, this paper proposes a deep learning-based pedestrian detection method for small and overlapping objects. The proposed method adopts a parallel feature pyramid network with multi-scale feature layers, and the multi-scale squeeze-and-excitation (MSSE) module is proposed for better selection of multi-scale features. The proposed MSSE module helps to detect small objects by increasing the final feature resolution. In addition, channel-wise feature representation emphasizes important channels with reduced influence of weakly related features. Finally, the object’s proposals are regressed using soft non-maximum suppression to differentiate the overlapped objects. The experiments show significant performance enhancement with the proposed method in an ablation study.