SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection
The use of machine learning (ML) methods for development of robust and flexible visual inspection system has shown promising. However their performance is highly dependent on the amount and diversity of training data. This is often restricted not only due to costs but also due to a wide variety of defects and product surfaces which occur with varying frequency. As such, one can not guarantee that the acquired dataset contains enough defect and product surface occurrences which are needed to develop a robust model. Using parametric synthetic dataset generation, it is possible to avoid these issues. In this work, we introduce a complete pipeline which describes in detail how to approach image synthesis for surface inspection - from first acquisition, to texture and defect modeling, data generation, comparison to real data and finally use of the synthetic data to train a defect segmentation model. The pipeline is in detail evaluated for milled and sandblasted aluminum surfaces. In addition to providing an in-depth view into each step, discussion of chosen methods, and presentation of ML results, we provide a comprehensive dual dataset containing both real and synthetic images.
Code (0)
등록된 구현이 없습니다.
Tasks
Dataset GenerationDiversityImage GenerationSimilar Papers 제목 키워드 기반
Text-VQA Aug: Pipelined Harnessing of Large Multimodal Models for Automated Synthesis
Creation of large-scale databases for Visual Question Answering tasks pertaining to the text data in a scene (text-VQA) involves skilful human annotation, which is tedious and challenging. With the advent of foundation m…
Visual Question AnsweringQuestion GenerationText SpottingVisual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline
Recent advancements in machine learning, particularly in deep learning and object detection, have significantly improved performance in various tasks, including image classification and synthesis. However, challenges per…
image-classificationImage ClassificationImage Generationobject-detection+1Enabling Robust, Real-Time Verification of Vision-Based Navigation through View Synthesis
This work introduces VISY-REVE: a novel pipeline to validate image processing algorithms for Vision-Based Navigation. Traditional validation methods such as synthetic rendering or robotic testbed acquisition suffer from …
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
Object viewpoint estimation from 2D images is an essential task in computer vision. However, two issues hinder its progress: scarcity of training data with viewpoint annotations, and a lack of powerful features. Inspired…
Image GenerationViewpoint EstimationPix2NeRF: Unsupervised Conditional p-GAN for Single Image to Neural Radiance Fields Translation
We propose a pipeline to generate Neural Radiance Fields (NeRF) of an object or a scene of a specific class, conditioned on a single input image. This is a challenging task, as training NeRF requires multiple views o…
3D-Aware Image SynthesisImage GenerationNeRFNovel View Synthesis+2