无损检测(NDT)是一种用于检查材料及其缺陷而不会损坏被测组件的技术。相控阵超声检测(PAUT)已成为工业无损检测应用中的热门话题。目前,超声数据的收集大部分是自动化的,而对数据的分析仍然主要是手动进行的。手动分析扫描图像缺陷效率低,容易出现不稳定,促使人们需要基于计算机的解决方案。基于深度学习的对象检测方法最近在解决此类挑战方面表现出了希望。这种方法通常需要大量的高分辨率,注释好的训练数据,这在无损检测中很难获得。因此,它变得难以检测低分辨率图像和具有变化的位置尺寸的缺陷。这项工作提出了基于最先进的YOLOv8算法的改进,以提高相控阵超声检测中缺陷检测的准确性和效率。引入空间深度卷积(SPD-Conv)来代替跨步卷积,减少卷积操作过程中的信息损失,提高低分辨率图像的检测性能。此外,本文构建了双层路由和空间注意力模块(BRSA)并将其整合到主干中,生成具有更丰富细节的多尺度特征图。在颈部,用渐近特征金字塔网络(AFPN)代替原来的结构,以减少模型参数和计算复杂度。在公共数据集上测试后,与YOLOv8(基线)相比,该算法在模拟数据集上实现了平底孔(FBH)和铝块的高质量检测。更重要的是,对于具有挑战性的检测缺陷侧钻孔(SDH),它实现了82.50%的F1得分(准确率和召回率的加权平均值)和65.96%的联合交集(IOU),分别改善17.56%和0.43%。在实验数据集上,FBH的F1得分和IOU分别达到75.68%(增加9.01%)和83.79%,分别。同时,所提出的算法在存在外部噪声的情况下表现出鲁棒性能,同时保持极高的计算效率和推理速度。这些实验结果验证了所提出的超声图像智能缺陷检测算法的高检测性能,这有助于智能行业的发展。
Non-destructive testing (NDT) is a technique for inspecting materials and their defects without causing damage to the tested components. Phased array ultrasonic testing (PAUT) has emerged as a hot topic in industrial NDT applications. Currently, the collection of ultrasound data is mostly automated, while the analysis of the data is still predominantly carried out manually. Manual analysis of scan image defects is inefficient and prone to instability, prompting the need for computer-based solutions. Deep learning-based object detection methods have shown promise in addressing such challenges recently. This approach typically demands a substantial amount of high-resolution, well-annotated training data, which is challenging to obtain in NDT. Consequently, it becomes difficult to detect low-resolution images and defects with varying positional sizes. This work proposes improvements based on the state-of-the-art YOLOv8 algorithm to enhance the accuracy and efficiency of defect detection in phased-array ultrasonic testing. The space-to-depth convolution (SPD-Conv) is imported to replace strided convolution, mitigating information loss during convolution operations and improving detection performance on low-resolution images. Additionally, this paper constructs and incorporates the bi-level routing and spatial attention module (BRSA) into the backbone, generating multiscale feature maps with richer details. In the neck section, the original structure is replaced by the asymptotic feature pyramid network (AFPN) to reduce model parameters and computational complexity. After testing on public datasets, in comparison to YOLOv8 (the baseline), this algorithm achieves high-quality detection of flat bottom holes (FBH) and aluminium blocks on the simulated dataset. More importantly, for the challenging-to-detect defect side-drilled holes (SDH), it achieves F1 scores (weighted average of precision and recall) of 82.50% and intersection over union (IOU) of 65.96%, representing an improvement of 17.56% and 0.43%. On the experimental dataset, the F1 score and IOU for FBH reach 75.68% (an increase of 9.01%) and 83.79%, respectively. Simultaneously, the proposed algorithm demonstrates robust performance in the presence of external noise, while maintaining exceptionally high computational efficiency and inference speed. These experimental results validate the high detection performance of the proposed intelligent defect detection algorithm for ultrasonic images, which contributes to the advancement of the smart industry.