当前行人检测的一个主要挑战是在复杂的场景中检测出不同尺度的行人, 尤其是远尺度行人。由于不同尺度的行人会表现出不同的视觉外观特征, 鉴于此提出了一种多尺度感知的行人检测算法。在全卷积网络结构中引进可形变卷积层, 扩大特征图的感受野; 通过级联区域建议网络提取多尺度行人建议区域, 引入多尺度判别策略, 定义尺度判别层, 判别行人建议区域的尺度类别; 构建一个多尺度感知网络, 引进软非极大值抑制（Soft-NMS）检测算法, 融合每个网络输出的分类值和回归值, 获取最终的行人检测结果。实验表明, 本文提出的检测算法在基准数据集Caltech和ETH上的检测误差较低, 检测精度优于当前其他检测算法, 适用于检测远尺度行人。

A major challenge of pedestrian detection is to detect different-scale pedestrians in complicated scenarios, especially for far-scale pedestrians. Motivated by the experiment that pedestrians with different scales exhibit dramatically different visual features, we propose in this paper a multi-scale aware pedestrian detection algorithm. Firstly, we introduce deformable convolutional layers in full convolutional network structure to expand the receptive field of feature maps. Secondly, we use cascade-region proposal network to extract multi-scale pedestrian proposals and introduce discriminant strategy, and define a multi-scale discriminant layer to distinguish pedestrian proposals category. Finally, we construct a multi-scale aware network, use the soft non-maximum suppression algorithm to fuse the output of classification score and regression offsets by each sensing network to generate final pedestrian detection regions. The experiments show that there is low detection error on the datasets Caltech and ETH, and the proposed algorithm is better than the current detection algorithms in terms of detection accuracy and works particularly well with far-scale pedestrians.