CPC G06T 7/0004 (2013.01) [G06T 7/73 (2017.01); G06T 2207/20016 (2013.01); G06T 2207/20081 (2013.01)] | 8 Claims |
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.
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