This paper aims to enhance the detection accuracy of fine linear fatigue cracks in aero-engine metallic components by developing an image enhancement and segmentation technique.
A dataset of fine linear fatigue cracks was constructed using image enhancement techniques. A U-shaped semantic segmentation network based on a Fully Convolutional Network (FCN) and pre-trained VGG-16 model was developed. Dice Loss, Focal Loss, and CBAM were employed to enhance crack segmentation precision.
The proposed method achieved a segmentation accuracy of 92.30% and successfully measured 95.91% of cracks with over 90% accuracy, demonstrating the effectiveness of the approach in improving crack detection and measurement.
The study focuses on small fatigue cracks, and future research may explore broader applications or the detection of other flaw types to enhance generalizability.
This work integrates advanced image processing techniques, attention mechanisms, and loss functions to improve fine crack segmentation, providing a novel method for accurately detecting small cracks in critical aerospace components.
