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.

中文翻译：

---

使用多尺度挤压和激励模块的行人检测

计算机视觉系统是自动驾驶汽车的主要研究项目。但是，了解道路场景通常是有挑战性的，尤其是当物体较小且重叠时。为了解决这些问题，本文提出了一种基于深度学习的行人检测方法，用于小而重叠的物体。所提出的方法采用具有多尺度特征层的并行特征金字塔网络，并提出了多尺度挤压和激励（MSSE）模块以更好地选择多尺度特征。建议的MSSE模块通过提高最终特征分辨率来帮助检测小物体。此外，逐通道特征表示强调具有弱相关特征影响的重要通道。最后，使用软非最大抑制对对象的建议进行回归，以区分重叠的对象。实验表明，该方法在消融研究中具有显着的性能增强。