Abstract: Defect detection has been revolutionized by the use of Convolutional Neural Networks (CNNs) for identifying defects in objects through image processing. While traditional CNN-based object detection algorithms have shown success in identifying natural objects, they often struggle when it comes to defect data. To tackle this challenge, a shared weight binary classification network is implemented to determine the presence of defects in images. This is then followed by a detection network that accurately locates the defects within the objects.
By utilizing this approach, the speed and accuracy of defect detection are significantly improved compared to conventional CNN-based object detection methods. This has been supported both theoretically and experimentally, demonstrating the effectiveness of the shared weight binary classification network in enhancing defect detection using CNN technology.
Keywords: CNN (Convolutional Neural Network), Image processing, Defect detection, Object detection, Shared weight binary classification network.
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DOI:
10.17148/IJARCCE.2024.134120
