Abstract
In this paper, we investigate and analyze applying several well-known models to the task of brands detection in images. Besides this we explore applying these models for solution of industrial task of detection objects on running production line.
In first parts of paper we compare and analyze most effective and widely used architectures as Faster R-CNN (based on Inception v2 and ResNet-50/101), SSD (based on Inception v2 and MobileNet). We implement such comparison using dataset of beer brands. The obtained results confirm the effectiveness of applying Faster R-CNN. But such models are resource intensive. In contrast SSD models perform processing faster than Faster R-CNN and can be considered as basic models for detection and segmentation of objects in images and video in real time.
Based on getting results of comparing models in the last parts of paper intelligent system for detection and recognition labeling of bottles in real time is given.
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Golovko, V., Kroshchanka, A., Mikhno, E. (2019). Brands and Caps Labeling Recognition in Images Using Deep Learning. In: Ablameyko, S., Krasnoproshin, V., Lukashevich, M. (eds) Pattern Recognition and Information Processing. PRIP 2019. Communications in Computer and Information Science, vol 1055. Springer, Cham. https://doi.org/10.1007/978-3-030-35430-5_4
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DOI: https://doi.org/10.1007/978-3-030-35430-5_4
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