In recent years, the performance of the convolutional neural network-based pedestrian detection method has improved significantly. However, an imbalance remains between detection accuracy and speed. In this paper, we employ a one-stage object detection framework and propose a pedestrian detection method based on the multi-scale attention mechanism of a convolutional neural network to improve the imbalance between accuracy and speed. First, a multi-scale convolution module is designed to extract corresponding features at different scales. Second, using the attention module, association information between features is mined from space and channel perspectives to strengthen the original features. Then, the enhanced features are passed through a classification and regression module to perform object positioning and bounding box regression. Finally, to learn more pedestrian location information, we improve the loss function to realise better network training. The proposed method achieved considerable results on the challenging CityPersons and Caltech pedestrian detection datasets.

近年来，基于卷积神经网络的行人检测方法的性能已大大提高。但是，检测精度和速度之间仍然存在不平衡。在本文中，我们采用了一个阶段的目标检测框架，并提出了一种基于卷积神经网络多尺度注意力机制的行人检测方法，以改善准确性和速度之间的不平衡。首先，设计多尺度卷积模块以提取不同尺度的相应特征。其次，使用注意力模块，从空间和通道的角度挖掘要素之间​​的关联信息，以增强原始要素。然后，将增强的功能传递给分类和回归模块，以执行对象定位和边界框回归。最后，为了了解更多行人位置信息，我们改进了丢失功能，以实现更好的网络训练。所提出的方法在具有挑战性的CityPersons和Caltech行人检测数据集上取得了可观的结果。