The invention requests to protect a multi-branch target detection method based on a traffic scene, and the method comprises the steps: S1, obtaining a high-definition picture taken by a traffic intersection to construct a related data set, carrying out the classification and marking of traffic scene images, generating a corresponding category label, and dividing a training set and a test set; s2,building a network model with 32 layers based on deep learning, obtaining nine anchor frame priori through a k-means clustering algorithm; averagely distributing the nine anchor frames into three detection branches; enabling the network to convert the detection task into a regression task; simultaneously completing the classification of the targets and the regression of the bounding boxes on one network; unifying four steps of candidate box generation, feature extraction, classification and position finishing of a target detection algorithm into a deep network framework, carrying out end-to-end training on a network model by adopting a back propagation and random gradient descent method, reducing a loss function to a small range through iterative training, and then stopping training.