Methods of performing real-time object detection using object real-time detection model, performance optimization methods of object real-time detection model, electronic devices and computer readable storage media

An electronic device, comprising:

• a processor;
• a memory for storing computer programs executable by the processor;
• wherein the processor is configured to execute the computer programs in the memory to:
• obtain an identification image of a preset size by pre-processing an input image obtain object central point data and object size data by processing the identification image using the object real-time detection model, wherein the object central point data represents a position of an object central point in the identification image and the object size data represents a width and a height of an object in the identification image;
• and
• obtain an object detection result by determining an object region in the input image according to the object central point data and the object size data;
• wherein when obtaining the object central point data and the object size data by processing the identification image using the object real-time detection model, the processor is caused to:
• extract a semantic feature map of at least one feature layer relating to the object from the identification image by using a feature extraction network in the object real-time detection model;
• obtain a target feature map by performing up-sampling for the semantic feature map of the at least one feature layer and performing fusion for feature maps obtained by up-sampling using a feature fusion network in the object real-time detection model;
• and
• obtain the object central point data and the object size data by extracting an object central point and an object size from the target feature map using a data obtaining network in the object real-time detection model;
• wherein the feature extraction network comprises a first ShuffleNet network, and the processor is further caused to:
• obtain a trained second ShuffleNet network by training a second ShuffleNet network using a training set;
• update the first ShuffleNet network using one or more network structure parameter values of the trained second ShuffleNet network;
• and
• obtain a trained first ShuffleNet network by training the updated first ShuffleNet network and take the trained first ShuffleNet network as the feature extraction network.