特征图聚集多尺度行人检测高效算法

针对使用人工设计特征训练的行人检测算法准确率和效率较低的问题，提出一种采用卷积神经网络特征图聚集多尺度行人检测高效算法. 设计一种特征图聚集网络，将高层次特征图与低层次特征图进行聚集，构造出有较好空间分辨和语义能力的特征图；构造特征延伸网络，提供用于多尺度行人检测的特征图；重新设计目标候选区域，构造多尺度行人检测网络，提升定位准确性，并将特征图聚集网络、特征延伸网络和多尺度行人检测网络组合进行端到端训练. 实验测试结果表明，该算法可以有效提高行人检测与定位准确性，并可在普通硬件设备条件下提供实时检测.

Efficient multi-scale pedestrian detection algorithm withfeature map aggregation

An efficient multi-scale pedestrian detection algorithm with convolutional neural network feature map aggregation was proposed for the problems of low accuracy and efficiency in pedestrian detection algorithm trained by manual design feature. An aggregation network was designed to gather high-level and low-level feature maps to construct a feature map with the ability of spatial resolution and semantic. And an extension network was constructed to provide feature maps for multi-scale detection. In addition, candidate areas were redesigned to construct a multi-scale detection network to improve positioning accuracy. The feature map aggregation network, extension network and multi-scale pedestrian detection network were combined for an end-to-end training. The experimental results show that, compared to algorithms based on manual design features, the proposed algorithm can effectively improve the accuracy of pedestrian detection and positioning. Under common hardware conditions, the proposed approach can provide real-time detection.