This application relates to the field of artificial intelligence, and specifically, to the field of computer vision, and discloses a perception network based on a plurality of headers. The perception network includes a backbone and the plurality of parallel headers. The plurality of parallel headers are connected to the backbone. The backbone is configured to receive an input image, perform convolution processing on the input image, and output feature maps, corresponding to the image, that have different resolutions. Each of the plurality of parallel headers is configured to detect a task object in a task based on the feature maps output by the backbone, and output a 2D box of a region in which the task object is located and confidence corresponding to each 2D box. Each parallel header detects a different task object. The task object is an object that needs to be detected in the task. Higher confidence indicates a higher probability that the task object corresponding to the task exists in the 2D box corresponding to the confidence.
OBJECT IDENTIFICATION METHOD AND DEVICE