Image processing method and related apparatus

An image processing apparatus, comprising a memory and a processor, wherein the memory stores code, the processor is configured to execute the code, and when the code is executed, the image processing apparatus performs the operations comprising:

• obtaining an input feature map of an image to be processed, wherein the input feature map comprises a first input sub-feature map and a second input sub-feature map, and resolution of the first input sub-feature map is higher than resolution of the second input sub-feature map;
• performing feature fusion processing on the input feature map by using a target network, to obtain an output feature map, wherein the target network is an adder neural network, the target network comprises a first branch and a second branch, the first branch is configured to perform feature extraction processing on the first input sub-feature map, to obtain a first sub-feature map and a first output sub-feature map, the first sub-feature map is an intermediate feature obtained by the first branch, the second branch is configured to perform feature extraction processing on the second input sub-feature map and the first sub-feature map, to obtain a second output sub-feature map, and the output feature map comprises the first output sub-feature map and the second output sub-feature map;
• and
• performing, based on the output feature map, object detection on the image to be processed, to obtain an object detection result.

A non-transitory computer storage medium, wherein the computer storage medium stores instructions, and when the instructions are executed by a computer, the computer is enabled to perform the operations comprising:

• obtaining an input feature map of an image to be processed, wherein the input feature map comprises a first input sub-feature map and a second input sub-feature map, and resolution of the first input sub-feature map is higher than resolution of the second input sub-feature map;
• performing feature fusion processing on the input feature map by using a target network, to obtain an output feature map, wherein the target network is an adder neural network, the target network comprises a first branch and a second branch, the first branch is configured to perform feature extraction processing on the first input sub-feature map, to obtain a first sub-feature map and a first output sub-feature map, the first sub-feature map is an intermediate feature obtained by the first branch, the second branch is configured to perform feature extraction processing on the second input sub-feature map and the first sub-feature map, to obtain a second output sub-feature map, and the output feature map comprises the first output sub-feature map and the second output sub-feature map;
• and
• performing, based on the output feature map, object detection on the image to be processed, to obtain an object detection result.