Detecting small defects in curved parts through classical monostatic pulse-echo ultrasonic imaging is known to be a challenge. Hence, a robot-assisted ultrasonic testing system with the track-scan imaging method is studied to improve the detecting coverage and contrast of ultrasonic images. To further improve the image resolution, we propose a visual geometry group-UNet (VGG-UNet) deep learning network to optimize the ultrasonic images reconstructed by the track-scan imaging method. The VGG-UNet uses VGG to extract advanced information from ultrasonic images and takes advantage of UNet for small dataset segmentation. A comparison of the reconstructed images on the simulation dataset with ground truth reveals that the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) can reach 39 dB and 0.99, respectively. Meanwhile, the trained network is also robust against the noise and environmental factors according to experimental results. The experiments indicate that the PSNR and SSIM can reach 32 dB and 0.99, respectively. The resolution of ultrasonic images reconstructed by track-scan imaging method is increased approximately 10 times. All the results verify that the proposed method can improve the resolution of reconstructed ultrasonic images with high computation efficiency.

中文翻译：

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Visual Geometric Group-UNet：用于弯曲零件的深度学习超声图像重建

通过经典的单基地脉冲回波超声成像技术检测弯曲零件中的小缺陷是一项挑战。因此，研究了一种采用轨迹扫描成像方法的机器人辅助超声测试系统，以提高超声图像的检测覆盖率和对比度。为了进一步提高图像分辨率，我们提出了视觉几何群-UNet（VGG-UNet）深度学习网络，以优化通过轨迹扫描成像方法重建的超声图像。VGG-UNet使用VGG从超声图像中提取高级信息，并利用UNet进行小型数据集分割。将模拟数据集上的重建图像与地面真实情况进行比较，发现峰值信噪比（PSNR）和结构相似性指标测度（SSIM）可以分别达到39 dB和0.99。同时，根据实验结果，训练有素的网络还具有强大的抗噪声和环境因素的能力。实验表明，PSNR和SSIM分别可以达到32 dB和0.99。通过轨迹扫描成像方法重建的超声图像的分辨率提高了约10倍。所有结果都证明该方法能够以较高的计算效率提高重建后的超声图像的分辨率。