RT-SPeeDet: Real-Time IP–CNN-Based Small Pit Defect Detection for Automatic Film Manufacturing Inspection

Author:

Ban Geunwoo,Yoo JoonhyukORCID

Abstract

Pits are defects that occur during the film manufacturing process; they appear in the micrometer scale, which makes distinguishing them with the human eye difficult. Existing defect detectors have poor recognition rates for small objects or require a considerable amount of time. To resolve these problems, we propose a real-time small pit defect detector (RT-SPeeDet), a two-stage detection model based on an image processing and convolutional neural network (IP–CNN) approach. The proposed method predicts boundary boxes using a lightweight image-processing algorithm optimized for pit defects, and applies binary classification to the predicted regions; thus, simultaneously simplifying the problem and achieving real-time processing speed, unlike existing detection methods that rely on CNN-based detectors for both boundary box prediction and classification. RT-SPeeDet uses lightweight image processing operations to extract pit defect candidate region image patches from high-resolution images. These patches are then passed through a CNN-based binary classifier to detect small pit defects at a real-time processing speed of less than 0.5 s. In addition, we propose a multiple feature map synthesis method that enhances the features of pit defects, enabling efficient detection of faint pit defects, which are particularly difficult to detect.

Funder

Daegu University

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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