English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a challenging computer vision task, namely the detectionof generic components on a PCB, and a novel set of deep-learning methods thatare able to jointly leverage the appearance of individual components and thepropagation of information across the structure of the board to accuratelydetect and identify various types of components on a PCB. Due to the expense ofmanual data labeling, a highly unbalanced distribution of component types, andsignificant domain shift across boards, most earlier attempts based ontraditional image processing techniques fail to generalize well to PCB imageswith various quality, lighting conditions, etc. Newer object detectionpipelines such as Faster R-CNN, on the other hand, require a large amount oflabeled data, do not deal with domain shift, and do not leverage structure. Toaddress these issues, we propose a three stage pipeline in which aclass-agnostic region proposal network is followed by a low-shot similarityprediction classifier. In order to exploit the data dependency within a PCB, wedesign a novel Graph Network block to refine the component features conditionedon each PCB. To the best of our knowledge, this is one of the earliest attemptsto train a deep learning based model for such tasks, and we demonstrateimprovements over recent graph networks for this task. We also provide in-depthanalysis and discussion for this challenging task, pointing to future research.

Data-Efficient Graph Embedding Learning for PCB Component Detection