Abstract: In the task of close range pedestrian detection, the balance of the precision and speed were of great significance to the practical application of the detection algorithm. In order to detect the close range target quickly and accurately, a pedestrian detection algorithm based on fused fully convolutional network was proposed. Firstly, a fully convolutional detection network was used to detect the target in the image, and a series of candidate bounding boxes were obtained. Secondly, pixel level classification results of the image were obtained by using a semantic segmentation network with weakly supervised training. Finally, the candidate bounding boxes and the pixel level classification results were fused to complete the detection. The experimental results show that the algorithm has good performance in both the speed and the precision of detection.