US 11,790,517 B2
Subtle defect detection method based on coarse-to-fine strategy
Jun Wang, Nanjing (CN); Zhongde Shan, Nanjing (CN); Shuyi Jia, Nanjing (CN); Dawei Li, Nanjing (CN); and Yuxiang Wu, Nanjing (CN)
Assigned to Nanjing University of Aeronautics and Astronautics, Nanjing (CN)
Filed by Nanjing University of Aeronautics and Astronautics, Nanjing (CN)
Filed on Apr. 24, 2023, as Appl. No. 18/306,166.
Claims priority of application No. 202210483136.7 (CN), filed on May 6, 2022.
Prior Publication US 2023/0260101 A1, Aug. 17, 2023
Int. Cl. G06T 7/00 (2017.01); G06T 7/73 (2017.01)
CPC G06T 7/0004 (2013.01) [G06T 7/73 (2017.01); G06T 2207/20016 (2013.01); G06T 2207/20081 (2013.01)] 8 Claims
OG exemplary drawing
 
1. A subtle defect detection method based on coarse-to-fine strategy, comprising:
(S1) acquiring data of an image to be detected via a charge-coupled device (CCD) camera;
(S2) constructing a defect area location network and preprocessing the image to be detected to initially determine a defect position;
(S3) constructing a defect point detection network; and training the defect point detection network by using a defect segmentation loss function; and
(S4) according to the defect position initially determined in step (S2), subjecting a subtle defect in the image to be detected to quantitative extraction and segmentation by using the defect point detection network;
wherein the defect point detection network comprises a backbone network comprising six stages, a bidirectional feature pyramid network, a classification network and a regression network;
an input image of the backbone network is an image output by the defect area location network, and the backbone network is configured to extract a defect feature of the input image;
in the six stages, a first stage comprises a convolutional layer and a 7×7 convolution kernel;
a second stage comprises a 3×3 max-pooling layer and a first dense block;
the second stage further comprises alternating 1×1 and 3×3 convolution kernels;
a third stage is composed of a second dense block; a fourth stage is composed of a third dense block structurally different from the second dense block;
the third stage and the fourth stage are configured to accelerate transmission of the defect feature and improve utilization of a defect feature image;
a fifth stage is composed of two dilated bottleneck layers to capture subtle target defect features; and
a sixth stage is composed of a dilated bottleneck layer to avoid loss of the subtle target defect features.