Deep Endoscope: Intelligent Duct Inspection for the Avionic Industry

Martelli, Samuele; Mazzei, Luca; Canali, C.; Guardiani, Paolo; Giunta, Salvatore; Ghiazza, Alberto; Mondino, Ivan; Cannella, Ferdinando; Murino, Vittorio; Bue, Alessio Del

doi:10.1109/tii.2018.2807797

Cited by 21 publications

(9 citation statements)

References 26 publications

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“…Liong et al [18] used AlexNet classification and the U-Net segmentation method to detect three kinds of defect data. Each collected image was separated into [26][27][28][29][30], they are not workable in wet-blue leather defect detection, or may not be effective even if they can be applied to as the defects in the wet-blue leather.…”

Section: Related Researchmentioning

confidence: 99%

Surface Defect Detection of Wet-Blue Leather Using Hyperspectral Imaging

et al. 2021

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Detection of surface defects on wet-blue leather is much more challenging than raw-hide leather. Since wet-blue leather turns blue and contains moisture after pre-treatment, it is a semi-product of the cowhide processing. At present, the defect detection of wet-blue leather is mostly carried out manually, and is time-consuming and labor-intensive for the professional inspectors. This paper is the first to use hyperspectral imaging (HSI) to implement the surface inspection of five wet-blue leather defects including brand masks, rotten grain, rupture, insect bites, and scratches in the pixel level detection. Hyperspectral Leather Defect Detection Algorithm (HLDDA) including Hyperspectral Target Detection (HTD) and Deep Learning (DL) techniques is proposed in this paper. In HTD, Weighted Background Suppression Constrained Energy Minimization (WBS-CEM) and WBS-Hierarchical CEM (WBS-hCEM) are developed in this paper by using weighting to suppress background and enhance the contrast between target and background. Experimental results show that the overall performance of WBS is better than the original CEM. In the DL part, 1D-Convolutional Neural Network (CNN), 2D-Unet and 3D-UNet architectures are designed to segment defect areas. For various characteristics of defects, 1D-CNN emphasizes defects with spectral features, 2D-Unet emphasizes defects with spatial features, and 3D-Unet can simultaneously process spatial and spectral information in HSI. Experimental results verified that the proposed HLDDA can effectively quantify and estimate the size of the defect, thereby accelerating the leather inspection process by professional inspectors and develop an automated leather grading towards Industry 4.0. INDEX TERMSConstrained Energy Minimization (CEM), Deep Learning (DL), Hyperspectral image (HSI)

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Section: Related Researchmentioning

confidence: 99%

Surface Defect Detection of Wet-Blue Leather Using Hyperspectral Imaging

et al. 2021

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“…Chen et al proposed a novel vision-based method that used a deep CNN in the detection of fastener defects [31]. Martelli et al used a DL-based spatiotemporal image analysis system to classify defects in metallic gearboxes [32]. However, these techniques are either based on image classification using DL models or depend heavily on annotated data acquired manually, which cannot predict defective regions using only image-level labeling.…”

Section: Related Workmentioning

confidence: 99%

A Weakly Supervised Surface Defect Detection Based on Convolutional Neural Network

Liang

Deng

et al. 2020

IEEE Access

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Surface defect detection is a critical task in product quality assurance for manufacturing lines. The deep learning-based methods recently developed for defect detection are typically trained using a supervised learning strategy and large defect sample sets. Conventional methods often require additional pixel-level labeling or bounding boxes to predict the location of defects. However, the number of required samples and the time-intensive annotation process limits the practical use of these algorithms. As such, this study proposes a weakly supervised detection framework in which a CNN model is trained to identify surface cracks in motor commutators. The model was trained using small subsets of defect samples (∼5-30) and does not require a pre-trained network. This approach consists of localization and decision networks that simultaneously predict both the location and probability of defects. A new loss function was also developed to identify abnormal regions in a sample with accessible image-level labels. A collaboration learning strategy was then applied to utilize the loss function and compensate for imbalances at the pixel level. Experimental results using a small number of image-level training labels from a real industrial dataset exhibited a 99.5% recognition accuracy, which is comparable to relevant methods using pixel-level labels.

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“…For instances, Boeing introduced a fault isolation system [3] to train operators to quickly learn troubleshooting techniques of the engine air bleed system. In the avionics industry, a new robotic endoscope [4] was introduced to automatically inspect different residuals (e.g., sand and metallic dust) inside the oil ducts of the metallic gearbox. Despite these achievements, however, due to the complex scenarios in aviation, there still lies several challenges to realize the automatic industrial inspection.…”

Section: Introductionmentioning

confidence: 99%

An AR-Assisted Deep Learning-Based Approach for Automatic Inspection of Aviation Connectors

Zheng

2021

IEEE Trans. Ind. Inf.

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The mismatched-pins inspection of complex aviation connector is a critical process to ensure the correct wiring harness assembly, of which the existing manual operation is error-prone and time-consuming. Aiming to fill this gap, this work proposes an augmented reality (AR)-assisted deep learning-based approach to tackle three major challenges in aviation connector inspection, including small pins detection, multi-pins sequencing, and mismatched pins visualization. Firstly, the proposed spatial-attention pyramid network approach extracts image features in multilayers and searches for their spatial relationships among images. Secondly, based on the cluster-generation sequencing algorithm, these detected pins are clustered into annuluses of expected layers and numbered according to their polar angles. Lastly, AR glass as the inspection visualization platform, highlights mismatched pins in the augmented interface to warn operators automatically. Compared with other existing methodologies, the experimental result shows that the proposed approach can achieve better performance accuracy, and support operator's inspection process efficiently.

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Deep Endoscope: Intelligent Duct Inspection for the Avionic Industry

Cited by 21 publications

References 26 publications

Surface Defect Detection of Wet-Blue Leather Using Hyperspectral Imaging

Surface Defect Detection of Wet-Blue Leather Using Hyperspectral Imaging

A Weakly Supervised Surface Defect Detection Based on Convolutional Neural Network

An AR-Assisted Deep Learning-Based Approach for Automatic Inspection of Aviation Connectors

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