An obstacle detection and depth estimation method based on deep learning for traffic scenarios was proposed. The method modifies the existing YOLOv3 model by replacing its small-scale transmission layer with DenseNet network, and obtains a new obstacle detection network Dense-YOLO. Then, the disparity map of binocular images was obtained by using the stereo matching network model PSMNet, and the depth of detected obstacles was estimated according to the binocular ranging principle. Extensive experiments were conducted on the KITTI dataset and the actual traffic images, and the results show that Dense-YOLO effectively improves the reliability and accuracy of the obstacle detection in traffic scenarios compared to YOLOv3. The average precision (AP) on the four-class of obstacles including car, pedestrian, cyclist and truck is increased by 3% to 5%, and the mean average precision (mAP) is increased by about 4%.The average relative error between the estimated depth of the obstacle and the true value is about 3%.