Object detection using deep learning methods in the field of intelligent transportation has become a research hotspot. Currently, most of the classic object detection algorithms, whether which are the single-stage object detection models based on regression or the two-stage object detection models based on candidate regions, use a large number of predefined priori boxes called “anchor” to enumerate the possible positions, sizes and aspect ratios so as to search the objects. It will cause serious imbalance between positive and negative samples, and the performance and generalization ability of the models are also limited by the anchor's design. Aiming at the above problems of the anchor-based object detection algorithms, a single-stage object detection network, called RetinaNet, is used to establish the anchor-free based object detection models for vehicles, pedestrians, and cyclists in traffic scenes. Pixel-by-pixel prediction is adopted to handle object detection and add central prediction branches to improve the detection performance. Experiments show that, compared with the original RetinaNet algorithm based on anchor, the improved algorithm can better recognize vehicles, pedestrians, and cyclists in traffic scenes.