A computer-implemented method for multi-task target detection, comprising: obtaining a detection image that depicts at least one target detection object; and inputting the detection image into a multi-task target detection model trained based on a step-by-step method, and detecting and identifying the target detection object, wherein the multi-task target detection model is obtained by step-by-step training of a pre-built model; the pre-built model comprises a main backbone network shared among a plurality of tasks, a plurality of branch backbone networks respectively used for each of a plurality of task groups, and a plurality of head networks respectively used for each task; and the step-by-step training of the pre-built model comprises: performing first-stage training on the pre-built model by using traffic scenario training images corresponding to a non-empty proper subset of tasks in each task group, thereby obtaining an initial trained model; freezing parameters of the main backbone network in the initial trained model, and performing second-stage training on the initial trained model by using traffic scenario training images corresponding to all tasks in each task group, thereby obtaining an intermediate trained model; and unfreezing parameters of the main backbone network in the intermediate trained model, and performing third-stage training on the intermediate trained model by using traffic scenario training images corresponding to all tasks in all task groups, thereby obtaining the multi-task target detection model; wherein different tasks in each task group correspond to different volumes of data of traffic scenario training images. obtaining a detection image that depicts at least one target detection object; and inputting the detection image into a multi-task target detection model trained based on a step-by-step method, and detecting and identifying the target detection object, wherein the multi-task target detection model is obtained by step-by-step training of a pre-built model; the pre-built model comprises a main backbone network shared among a plurality of tasks, a plurality of branch backbone networks respectively used for each of a plurality of task groups, and a plurality of head networks respectively used for each task; and the step-by-step training of the pre-built model comprises: performing first-stage training on the pre-built model by using traffic scenario training images corresponding to a non-empty proper subset of tasks in each task group, thereby obtaining an initial trained model; freezing parameters of the main backbone network in the initial trained model, and performing second-stage training on the initial trained model by using traffic scenario training images corresponding to all tasks in each task group, thereby obtaining an intermediate trained model; and unfreezing parameters of the main backbone network in the intermediate trained model, and performing third-stage training on the intermediate trained model by using traffic scenario training images corresponding to all tasks in all task groups, thereby obtaining the multi-task target detection model; wherein different tasks in each task group correspond to different volumes of data of traffic scenario training images. performing first-stage training on the pre-built model by using traffic scenario training images corresponding to a non-empty proper subset of tasks in each task group, thereby obtaining an initial trained model; freezing parameters of the main backbone network in the initial trained model, and performing second-stage training on the initial trained model by using traffic scenario training images corresponding to all tasks in each task group, thereby obtaining an intermediate trained model; and unfreezing parameters of the main backbone network in the intermediate trained model, and performing third-stage training on the intermediate trained model by using traffic scenario training images corresponding to all tasks in all task groups, thereby obtaining the multi-task target detection model; wherein different tasks in each task group correspond to different volumes of data of traffic scenario training images.