In order to improve the accuracy of the Tiny YOLOV3 target detection algorithm in pedestrian detection tasks, the algorithm is researched and improved. Firstly, deepen the feature extraction network of Tiny YOLOV3 to enhance the feature extraction capabilities of the network. Then, add the channel attention mechanism to the two detection scales of the prediction network, and assign different weights to different channels of the feature map to guide the network to pay more attention the visible area of pedestrians. Finally, the activation function and loss function are improved, and the K-means clustering algorithm is used to reselect the initial candidate frame. Experimental results show that the improved Tiny YOLOV3 algorithm has an average precision(AP) of 77% on the VOC2007 pedestrian subset and 92.7% on the INRIA data set, which is 8.5% and 2.5% higher than Tiny YOLOV3, and the running speed is 92.6 frame per second(FPS) and 31.2 FPS. The improved algorithm improves the accuracy of pedestrian detection, maintains a faster detection speed, and meets real-time operation requirements.