US 11,941,844 B2
Object detection model generation method and electronic device and computer readable storage medium using the same
Yepeng Liu, Shenzhen (CN); Yusheng Zeng, Shenzhen (CN); Jun Cheng, Shenzhen (CN); Jing Gu, Shenzhen (CN); Yue Wang, Shenzhen (CN); and Jianxin Pang, Shenzhen (CN)
Assigned to UBTECH ROBOTICS CORP LTD, Shenzhen (CN)
Filed by UBTECH ROBOTICS CORP LTD, Shenzhen (CN)
Filed on Aug. 17, 2021, as Appl. No. 17/403,902.
Application 17/403,902 is a continuation of application No. PCT/CN2020/140412, filed on Dec. 28, 2020.
Claims priority of application No. 202010778791.6 (CN), filed on Aug. 5, 2020.
Prior Publication US 2022/0044438 A1, Feb. 10, 2022
Int. Cl. G06T 7/73 (2017.01); G06N 3/04 (2023.01); G06V 40/16 (2022.01)
CPC G06T 7/74 (2017.01) [G06N 3/04 (2013.01); G06T 7/75 (2017.01); G06V 40/164 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30201 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An object detection model generation method, comprising steps of:
inputting a preset first amount of training samples into a to-be-trained object detection model, and performing an iterative training on the object detection model, wherein the training samples include one or more positive samples and one or more negative samples;
determining, an untrained current iteration node as an object iteration node according to a node order, and obtaining one or more model parameters of the object detection model corresponding to a previous iteration node of the object iteration node;
determining a detection accuracy of the object detection model at the object iteration node based on the one or more model parameters;
obtaining one or more enhanced negative samples by enhancing one or more mis-detected negative samples of the object detection model at the object iteration node according to a preset negative sample enhancement rule, in response to the detection accuracy being less than or equal to a preset accuracy threshold;
training the object iteration node based on the one or more enhanced negative samples and the preset first amount of the training samples; and
returning to the determining the untrained current iteration node as the object iteration node according, to the node order after the object iteration node is trained until the object detection model is trained;
wherein the obtaining the one or more enhanced negative samples by enhancing the one or more mis-detected negative samples of the object detection model at the object iteration node according to the preset negative sample enhancement rule comprises:
obtaining the one or more mis-detected negative samples of the object detection model at the object iteration node;
obtaining one or more spliced images by splicing a preset second amount of the mis-detected negative samples and the positive samples at intervals; and
obtaining the one or more enhanced negative samples by cropping all the spliced images according to a preset cropping rule.