7,345 research outputs found
DeepKey: Towards End-to-End Physical Key Replication From a Single Photograph
This paper describes DeepKey, an end-to-end deep neural architecture capable
of taking a digital RGB image of an 'everyday' scene containing a pin tumbler
key (e.g. lying on a table or carpet) and fully automatically inferring a
printable 3D key model. We report on the key detection performance and describe
how candidates can be transformed into physical prints. We show an example
opening a real-world lock. Our system is described in detail, providing a
breakdown of all components including key detection, pose normalisation,
bitting segmentation and 3D model inference. We provide an in-depth evaluation
and conclude by reflecting on limitations, applications, potential security
risks and societal impact. We contribute the DeepKey Datasets of 5, 300+ images
covering a few test keys with bounding boxes, pose and unaligned mask data.Comment: 14 pages, 12 figure
Information embedding and retrieval in 3D printed objects
Deep learning and convolutional neural networks have become the main tools of computer vision. These techniques are good at using supervised learning to learn complex representations from data. In particular, under limited settings, the image recognition model now performs better than the human baseline. However, computer vision science aims to build machines that can see. It requires the model to be able to extract more valuable information from images and videos than recognition. Generally, it is much more challenging to apply these deep learning models from recognition to other problems in computer vision.
This thesis presents end-to-end deep learning architectures for a new computer vision field: watermark retrieval from 3D printed objects. As it is a new area, there is no state-of-the-art on many challenging benchmarks. Hence, we first define the problems and introduce the traditional approach, Local Binary Pattern method, to set our baseline for further study. Our neural networks seem useful but straightfor- ward, which outperform traditional approaches. What is more, these networks have good generalization. However, because our research field is new, the problems we face are not only various unpredictable parameters but also limited and low-quality training data.
To address this, we make two observations: (i) we do not need to learn everything from scratch, we know a lot about the image segmentation area, and (ii) we cannot know everything from data, our models should be aware what key features they
should learn. This thesis explores these ideas and even explore more. We show how to use end-to-end deep learning models to learn to retrieve watermark bumps and tackle covariates from a few training images data. Secondly, we introduce ideas from synthetic image data and domain randomization to augment training data and understand various covariates that may affect retrieve real-world 3D watermark bumps. We also show how the illumination in synthetic images data to effect and even improve retrieval accuracy for real-world recognization applications
Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches
Additive manufacturing (AM) is gaining attention across various industries
like healthcare, aerospace, and automotive. However, identifying defects early
in the AM process can reduce production costs and improve productivity - a key
challenge. This study explored the effectiveness of machine learning (ML)
approaches, specifically transfer learning (TL) models, for defect detection in
3D-printed cylinders. Images of cylinders were analyzed using models including
VGG16, VGG19, ResNet50, ResNet101, InceptionResNetV2, and MobileNetV2.
Performance was compared across two datasets using accuracy, precision, recall,
and F1-score metrics. In the first study, VGG16, InceptionResNetV2, and
MobileNetV2 achieved perfect scores. In contrast, ResNet50 had the lowest
performance, with an average F1-score of 0.32. Similarly, in the second study,
MobileNetV2 correctly classified all instances, while ResNet50 struggled with
more false positives and fewer true positives, resulting in an F1-score of
0.75. Overall, the findings suggest certain TL models like MobileNetV2 can
deliver high accuracy for AM defect classification, although performance varies
across algorithms. The results provide insights into model optimization and
integration needs for reliable automated defect analysis during 3D printing. By
identifying the top-performing TL techniques, this study aims to enhance AM
product quality through robust image-based monitoring and inspection
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