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The three-dimensional reconstruction of high-gloss/reflection and low-texture objects (e.g., oil casing threads) is a complex task. In this paper, we present a novel approach that combines convolutional neural networks (CNNs) and multi-layer perception (MLP) with traditional three-dimensional reconstruction methods, thereby enhancing the detection efficiency. Our method utilizes a dataset of 800 samples that includes a variety of thread defects to train a U-net-like model as a three-dimensional reconstructor. Then, an MLP model is proposed to improve the accuracy of the three-dimensional reconstructed thread profile to the level of three-coordinate measurements through a regression analysis. The experimental results demonstrate that the method can effectively detect the black-crested threads of oil casing threads and quantify their proportions in the entire sample for accurate quality assessment. The method is easy to operate and can detect black threads effectively, providing a powerful tool for oil companies to ensure exploration benefits.

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