Recurrent ensemble boosting based method for detecting small and transparent foreign bodies in semi-opaque containers

Djallel Belhadj; Danielle Nuzillard; Valeriu Vrabie; Madjid Haddad

doi:10.1117/12.2521741

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16 July 2019 Recurrent ensemble boosting based method for detecting small and transparent foreign bodies in semi-opaque containers

Djallel Belhadj, Danielle Nuzillard, Valeriu Vrabie, Madjid Haddad

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Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 111721H (2019) https://doi.org/10.1117/12.2521741
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

Abstract

In recent years, driven by new standards and brand image, performant quality control has become highly important in the beverage industry. In the absence of reliable and affordable methods for detecting foreign bodies in semi-opaque bottles, many manufacturers still opt for human visual inspection on the production line. Advanced technological systems such as X-ray based systems started to be used but such technologies are expensive and intrusive. The visible domain offers an alternative and advantageous solution. In this work a recurrent boosting method is proposed and extended to dynamic information to detect small transparent objects. The proposed method works on images video acquired by an RGB camera, it considers an ensemble classification strategies that includes temporal and dynamical features in addition to the classical spatial image features such as texture and color. The experimental results show a better performance with time series and dynamic information, the effectiveness of the method is demonstrated in detection of random foreign objects regardless their size or transparency in different semi-opaque bottles.

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Djallel Belhadj, Danielle Nuzillard, Valeriu Vrabie, and Madjid Haddad "Recurrent ensemble boosting based method for detecting small and transparent foreign bodies in semi-opaque containers", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111721H (16 July 2019); https://doi.org/10.1117/12.2521741

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