Image quality enhancement using HybridGAN for automated railway track defect recognition

Cheng, Ming; Khasani, Riqi Radian; Setiono, Kent

doi:10.1016/j.autcon.2022.104669

Cited by 8 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With regard to the automatic detection of target objects based on the UAV images, the You Only Look Once (YOLO) series which demonstrated high accuracy and efficiency for onsite inspection has been developed consequently and successfully applied to civil engineering tasks, such as the detection of road surface damage [24], bridge cracks [25] and railway surfaces [26] in recent years. In the field of autonomous visual inspection for WTB, Qiu et al [27] proposed a YOLO-based small object detection approach (YSODA) by combining CNN and YOLO and achieved successful smallsized defect detection results.…”

Section: Introductionmentioning

confidence: 99%

Inspection of wind turbine blades using image deblurring and deep learning segmentation

Lu,

Gao,

Zhou

2024

Health Monitoring of Structural and Biological Systems XVIII

View full text Add to dashboard Cite

Remote and complex work sites of wind turbines limit the accessibility of the condition assessment. Wind turbine blades are subject to sustained wind load and harsh natural environmental conditions, which are vulnerable to various faults. Robotic-enabled sensing technology appears to be a promising solution for an efficient wind turbine blade inspection. Together with the recent advances in image processing and deep learning segmentation, automated inspection of wind turbine blades becomes possible. Nevertheless, it remains a challenging task to quantify the damage accurately due to the complex condition of images concerning motion blurs. To address this issue, an integrated framework, i.e., the combination of a Deblur Generative Adversarial Network v2 (DeblurGAN-v2) and You Only Look Once v8 (YOLO-v8) was proposed in this study. Specifically, the mapping between the motion-blurred images and those in high quality was adopted from the open-access pretrained DeblurGAN-v2, based on which the deblurring performance for wind turbine images with various motion blur scales was discussed concerning the image quality. Subsequently, the transfer learning method was implemented to fine-tune YOLO-v8. The well-trained YOLO v8 was then utilized for target defect segmentation on the deblurred images. Finally, various metrics were calculated to evaluate the segmentation accuracy and efficiency. Results prove a good generalization of DeblurGAN-v2 on wind turbine images and clearly illustrate the enhanced performance of the proposed methodology especially when the motion blur scale is within 35. The integrated framework could serve as a reference for dealing with other fuzzy image-related issues.

show abstract