Target recognition method and device based on MASK RCNN network model

A target recognition device based on a MASK RCNN network model, comprising:

• a network determining unit for determining a multi-stage network as a basic network;
• a model building unit for selecting at least one intermediate layer capable of extracting a feature map from the basic network, and inputting respectively a feature map output by the intermediate layer and a feature map output by an end layer of the basic network to corresponding MASK RCNN recognition networks to construct a network model based on the MASK RCNN, wherein the feature map output by the intermediate layer and the feature map output by the end layer have different sizes;
• a model training unit for training the MASK RCNN recognition networks with a data set and stopping training until a preset training end condition is satisfied;
• and
• a target recognition unit for recognizing a target using the MASK RCNN recognition networks after trained.

An electronic apparatus, comprising:

• a processor;
• and a memory arranged to store computer executable instructions, wherein the executable instructions, when executed, cause the processor to perform the following processing:
• determining a multi-stage network as a basic network;
• selecting at least one intermediate layer capable of extracting a feature map from the basic network, and inputting respectively a feature map output by the intermediate layer and a feature map output by an end layer of the basic network to corresponding MASK RCNN recognition networks to construct a network model based on the MASK RCNN, wherein the feature map output by the intermediate layer and the feature map output by the end layer have different sizes;
• training the MASK RCNN recognition networks with a data set and stopping training until a preset training end condition is satisfied;
• and
• recognizing a target using the MASK RCNN recognition networks after trained.