Abstract:  A pedestrian detection method was proposed based on convolutional neural network to improve the accuracy of pedestrian detection. The method took a YOLOv3-tiny algorithm as a base. In the backbone network part, in order to deepen the network depth, an original convolutional network structure was replaced by a depthwise separable convolution. In the detection part, an improved multi-level feature pyramid network was proposed. The network consisted of eight feature pyramids with the same structure. The feature pyramid was also composed of depthwise separable convolutions. The feature pyramid was connected in series, features of the same size obtained by different pyramids were merged. Then the fused feature pyramid was used for detection. Tests on a Caltech Pedestrian dataset were done. The results show that the miss rate of this method is 57.83%, which is 32.53% lower than that of the histogram of oriented gradient (HOG) method, and 4.67%, 3.21% lower than that of the deep learning method SA Fast-RCNN and MS-CNN, with a running speed of 34 ms/frame. Therefore, this method meets the real-time requirement.

Key words: automotive active safety , pedestrian detection , depthwise separable convolution , multi-level feature pyramid network , feature fusio