Autonomous detection of damage to multiple steel surfaces from 360° panoramas using deep neural networks

Luo, Cai; Yu, Leijian; Yan, Jiaxing; Li, Zhongwei; Ren, Peng; Bai, Xiao; Yang, Erfu; Liu, Yonghong

doi:10.1111/mice.12686

Cited by 47 publications

(32 citation statements)

References 54 publications

(69 reference statements)

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“…Luo et al. (2021) proposed a panoramic surface damage detection network to achieve the detection of the damage to multiple steel surfaces. Feng et al.…”

Section: Related Workmentioning

confidence: 99%

“…P. Wang et al (2021) used the SSD algorithm to extract the characteristics of tunnel lining cracks and subsequently avoid the influence of noise. Luo et al (2021) proposed a panoramic surface damage detection network to achieve the detection of the damage to multiple steel surfaces. Feng et al (2021) employed separable convolution and asymmetric convolution to construct an spillway tunnel defect detection (STDD) network and realized the accurate identification of rebarexposed in a tunnel.…”

Section: Related Workmentioning

confidence: 99%

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Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

Zhou

Zhang

Gong

2022

Computer aided Civil Eng

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Aiming to solve the challenges of low detection accuracy, poor anti-interference ability, and slow detection speed in the traditional tunnel lining defect detection methods, a novel deep learning-based model, named You Only Look Once network v4 enhanced by EfficientNet and depthwise separable convolution (DSC; YOLOv4-ED), is proposed. In the YOLOv4-ED, EfficientNet is used as the backbone to improve the identification accuracy of indistinguishable defect targets in complex tunnel background and light conditions. Furthermore, DSC block is introduced to reduce the storage space of the model and thereby enhance the detection efficiency. The experimental results indicate that the mean average precision, F1 score, Model Size, and FPS of YOLOv4-ED are 81.84%, 81.99%, 49.3 MB, and 43.5 f/s, respectively, which is superior to the comparison models in both detection accuracy and efficiency. Based on robust and cost-effective YOLOv4-ED, a tunnel lining defect detection platform (TLDDP) with the capacity of automated inspection of various lining defects (i.e., water leakage, crack, rebar-exposed) is built. The established TLDDP can realize the high-precision and automatic detection of multiple tunnel lining defects under different lighting and complex background conditions of the practical in-service tunnel.

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“…Luo et al. (2021) proposed a panoramic surface damage detection network to achieve the detection of the damage to multiple steel surfaces. Feng et al.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

Zhou

Zhang

Gong

2022

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…Researchers started concentrating on the topic of deep neural networks and how to apply them to object detection after Krizhevsky developed AlexNet in 2012 to obtain outstanding results in image classification [38] [36]. Machine vision technology in convergence with artificial intelligence has been rapidly improving and is helping agricultural, industrial, medical and other complex real-time applications [3][4] [5] [6].…”

Section: Object Detectionmentioning

confidence: 99%

An Analysis of Defect Detection on Steel Strip Surface Using Modified Yolo V5

Prabadevi

2022

Preprint

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Defects in any type of material do lessen the strength and will diminish the service life which in-turn affects the quality. Steel surface images with broad scale variance and complicated background lessens the high quality and productiveness. In steel surface images detection, there are hitches of small target sizes and imprecise features that causes incorrect and ignored detection. Nevertheless, these problems can be avoided with the aid of inspecting the defects earlier than processing with accurate and automated object detection algorithms. YOLOv5, a fine tuned learning model is evaluated on available steel strip datasets, which solves identification and detection problems in industrial scenario. The current YOLOv5 research focus on improving accuracy on defect classification and detection speed however there’s no distinct records on algorithm execution time and size. This study focus on YOLOv5 model with induced GHOST and TRANSFORMER modules. Through the new modules, we are looking to reduce the size of the algorithm, number of parameters and number of GFLOPS. By these changes this model may be deployed easily on mobile, embedded devices for detection of small, easily confused defects.

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“…Furthermore, the new PAnoramic surface damage DEtection Network (PADENet) has been reported to assess corrosion from metal structures. The PADENet method is based on an unmanned aerial vehicle (UAV) for capturing panoramic images, a distorted panoramic augmentation method, the use of multiple projection techniques, a modified CNN (faster region-based), and training through transfer learning on VGG-16 [30].…”

Section: Introductionmentioning

confidence: 99%

Civil Infrastructure Damage and Corrosion Detection: An Application of Machine Learning

et al. 2022

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Automatic detection of corrosion and associated damages to civil infrastructures such as bridges, buildings, and roads, from aerial images captured by an Unmanned Aerial Vehicle (UAV), helps one to overcome the challenges and shortcomings (objectivity and reliability) associated with the manual inspection methods. Deep learning methods have been widely reported in the literature for civil infrastructure corrosion detection. Among them, convolutional neural networks (CNNs) display promising applicability for the automatic detection of image features less affected by image noises. Therefore, in the current study, we propose a modified version of deep hierarchical CNN architecture, based on 16 convolution layers and cycle generative adversarial network (CycleGAN), to predict pixel-wise segmentation in an end-to-end manner using the images of Bolte Bridge and sky rail areas in Victoria (Melbourne). The convolutedly designed model network proposed in the study is based on learning and aggregation of multi-scale and multilevel features while moving from the low convolutional layers to the high-level layers, thus reducing the consistency loss in images due to the inclusion of CycleGAN. The standard approaches only use the last convolutional layer, but our proposed architecture differs from these approaches and uses multiple layers. Moreover, we have used guided filtering and Conditional Random Fields (CRFs) methods to refine the prediction results. Additionally, the effectiveness of the proposed architecture was assessed using benchmarking data of 600 images of civil infrastructure. Overall, the results show that the deep hierarchical CNN architecture based on 16 convolution layers produced advanced performances when evaluated for different methods, including the baseline, PSPNet, DeepLab, and SegNet. Overall, the extended method displayed the Global Accuracy (GA); Class Average Accuracy (CAC); mean Intersection Of the Union (IOU); Precision (P); Recall (R); and F-score values of 0.989, 0.931, 0.878, 0.849, 0.818 and 0.833, respectively.

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Autonomous detection of damage to multiple steel surfaces from 360° panoramas using deep neural networks

Cited by 47 publications

References 54 publications

Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

An Analysis of Defect Detection on Steel Strip Surface Using Modified Yolo V5

Civil Infrastructure Damage and Corrosion Detection: An Application of Machine Learning

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