Detection of Glass Surface Using Reflection Characteristic

Sudo, Hiroki; Yukushige, Shuta; Muramatsu, Satoshi; Inagaki, Katsuhiko; Chugo, Daisuke; Hashimoto, Hiroshi

doi:10.1109/iecon48115.2021.9589219

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…In reflection detection, reflection features are utilised as intrinsic features. Sudo et al [ 25 ] proposed a glass detection method by focusing on the reflective properties of the glass surface. Wu et al [ 26 ] used reflection features to track and locate moving objects in videos and proved the contribution of reflection features to detecting non-line-of-sight objects.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model

et al. 2023

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For remanufacturing to be more economically attractive, there is a need to develop automatic disassembly and automated visual detection methods. Screw removal is a common step in end-of-life product disassembly for remanufacturing. This paper presents a two-stage detection framework for structurally damaged screws and a linear regression model of reflection features that allows the detection framework to be conducted under uneven illumination conditions. The first stage employs reflection features to extract screws together with the reflection feature regression model. The second stage uses texture features to filter out false areas that have reflection features similar to those of screws. A self-optimisation strategy and weighted fusion are employed to connect the two stages. The detection framework was implemented on a robotic platform designed for disassembling electric vehicle batteries. This method allows screw removal to be conducted automatically in complex disassembly tasks, and the utilisation of the reflection feature and data learning provides new ideas for further research.

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Section: Related Workmentioning

confidence: 99%