基于级联网络的行人检测方法

Pedestrian detection method based on cascade networks

针对复杂环境下行人检测不能同时满足高召回率与高效率检测的问题,提出一种基于卷积神经网络(CNN)的行人检测方法。首先,采用CNN中的单步检测升级版网络YOLOv2初步检测行人;然后,设计一个网络与YOLOv2网络级联。设计的网络具有目标分类和边界框回归的功能,对YOLOv2初步检测出的行人位置进行再分类与回归,以此降低误检,提高召回率;最后,采用非极大值抑制(NMS)处理的方法去除冗余的边界框。实验结果显示,在数据集INRIA和Caltech上,所提方法与原始YOLOv2相比,召回率提高3. 3个百分点,准确率提高5. 1个百分点,同时速度上达到了11. 6帧/s,实现了实时检测。与现有的流行的行人检测方法相比,所提方法具有更好的整体性能。

In complex environment, existing pedestrian detection methods can not be very good to achieve high recall rate and efficient detection. To solve this problem, a pedestrian detection method based on Convolutional Neural Network( CNN)was proposed. Firstly, pedestrian locations in input images were initially detected with single step detection upgrade network( YOLOv2) derived from CNN. Secondly, a network with target classification and bounding box regression was designed to cascade with YOLOv2 network, which made reclassification and regression of pedestrian location initially detected by YOLOv2, to reduce error detections and increase recall rate. Finally, a Non-Maximum Suppression( NMS) method was used to remove redundant bounding boxes. The experimental results show that, in INRIA and Caltech dataset, the proposed method increases recall rate by 3. 3 percentage points, and the accuracy is increased by 5. 1 percentage points compared with original YOLOv2. It also reached a speed of 11. 6FPS( Frames Per Second) to realize real-time detection. Compared with the existing six popular pedestrian detection methods, the proposed method has better overall performance.