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 proposes a novel approach to real-time automatic rim detection,classification, and inspection by combining traditional computer vision anddeep learning techniques. At the end of every automotive assembly line, aquality control process is carried out to identify any potential defects in theproduced cars. Common yet hazardous defects are related, for example, toincorrectly mounted rims. Routine inspections are mostly conducted by humanworkers that are negatively affected by factors such as fatigue or distraction.We have designed a new prototype to validate whether all four wheels on asingle car match in size and type. Additionally, we present three comprehensiveopen-source databases, CWD1500, WHEEL22, and RB600, for wheel, rim, and boltdetection, as well as rim classification, which are free-to-use for scientificpurposes.

Real-Time Wheel Detection and Rim Classification in Automotive Production