Method and apparatus of training object detection network and object detection method and apparatus

An electronic device, comprising:

• one or more memory;
• and
• one or more processors;
• wherein the one or more memory and the one or more processors are connected with each other;
• and
• the one or more memory stores computer-executable instructions for controlling the one or more processors to:
• input a training image into a to-be-trained object detection network to obtain detection information of a target object in the training image, wherein the detection information comprises a detection class of the target object, a detection position of a detection box of the target object, and a detection position of a landmark of the target object inside the detection box;
• calculate a total loss function of the to-be-trained object detection network, wherein the total loss function is calculated according to a first loss function of the detection class of the target object, a second loss function of the detection position of the detection box of the target object, and a third loss function of the detection position of the landmark of the target object inside the detection box;
• and
• adjust a parameter of the to-be-trained object detection network according to the total loss function of the to-be-trained object detection network, to obtain a trained object detection network.

An electronic device, comprising:

• one or more memory;
• and
• one or more processors;
• wherein the one or more memory and the one or more processors are connected with each other;
• and
• the one or more memory stores computer-executable instructions for controlling the one or more processors to input a to-be-detected image into an object detection network, and outputting a detection position and a detection class of a detection box of a target object in the to-be-detected image;
• wherein the one or more memory further stores computer-executable instructions for controlling the one or more processors to train the object detection network by:
• inputting a training image into a to-be-trained object detection network to obtain detection information of a target object in the training image, wherein the detection information comprises a detection class of the target object, a detection position of a detection box of the target object, and a detection position of a landmark of the target object inside the detection box;
• calculating a total loss function of the to-be-trained object detection network, wherein the total loss function is calculated according to a first loss function of the detection class of the target object, a second loss function of the detection position of the detection box of the target object, and a third loss function of the detection position of the landmark of the target object inside the detection box;
• and
• adjusting a parameter of the to-be-trained object detection network according to the total loss function of the to-be-trained object detection network, to obtain a trained object detection network.