engleski

DefectDet: a deep learning architecture for detection of defects with extreme aspect ratios in ultrasonic images

Non-destructive testing (NDT) is a set of techniques used for material inspection and detection of defects. Ultrasonic testing (UT) is one of the NDT techniques, commonly used to inspect components in the oil and gas industry, aerospace, and various types of power plants. Acquisition of the UT data is currently done automatically using robotic manipulators. This ensures the precision and uniformity of the acquired data. On the other hand, the analysis is still done manually by trained experts. Since the acquired UT data can be represented in the form of images, computer vision algorithms can be applied to analyze the content of images and localize defects. In this work, we propose a novel deep learning architecture designed specifically for defect detection from UT images. We propose a lightweight feature extractor that improves the precision and efficiency of the detector. We also modify the detection head to improve the detection of the objects with extreme aspect ratios which are common in UT images. We tested our approach on an in-house dataset with over 4000 images. The proposed architecture outperformed the previous state-of-the-art method by 1.7% (512x512 px input resolution) and 2.7% (384x384 px input resolution) while significantly decreasing the inference time.

image analysis ; convolutional neural networks ; non-destructive testing ; ultrasonic imaging ; defect detection

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