Abstract
Object detection is being applied in an increasing number of areas. In this paper, the authors investigate the application of object detection in a use case for the kitchen industry. The main goal of the use case is to extract information from kitchen scenes that can be used for kitchen planning. The use case is located in a medium-sized company that has little experience with the application of deep learning models. Therefore, this paper proposes a methodology that ensures fast and reliable testing of different object detection models to identify a suitable model for the given use case. In the first step, a dataset with kitchen images is built. Further, augmentation methods are applied to the dataset, to increase the amount and variety of the data. For object detection, there is a variety of models that are freely available, and the question of which model is best for the use case cannot be answered easily. A selection of models (Faster R-CNN, SSD, and EfficentDet) from the TensorFlow Object Detection API will therefore be tested on the image dataset created. The achieved mean average precision (mAP) of the trained models will be used as a metric to determine the best model for the use case. The purpose of this work is to provide an approximate solution, that proves that object detection and the methodology work for the use case.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dahm, M.H., Constantine, B.: Machine Learning für den Mittelstand. In: Dahm, M.H., Thode, S. (eds.) Digitale Transformation in der Unternehmenspraxis, pp. 327–344. Springer, Wiesbaden (2020). https://doi.org/10.1007/978-3-658-28557-9_16
Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. Das umfassende Handbuch: Grundlagen, aktuelle Verfahren und Algorithmen, neue Forschungsansaetze. mitp Professional. MITP, Frechen, 1 edn, February 2018. ISBN 978-3-95845-700-3
Hassaballah, M., Awad, A.I.: Deep Learning in Computer Vision: Principles and Applications. Digital Imaging and Computer Vision. CRC Press, Boca Raton (2020). ISBN 978-1-138-54442-0
Jiang, X., Hadid, A., Pang, Y., Granger, E., Feng, X.: Deep Learning in Object Detection and Recognition. Springer, Singapore (2019)
Kang, L.-W., Wang, I.-S., Chou K.-L., Chen, S.-Y., Chang, C.-Y.: Image-based real-time fire detection using deep learning with data augmentation for vision-based surveillance applications. In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–4, Taipei, Taiwan, September 2019, IEEE. ISBN 978-1-72810-990-9. https://doi.org/10.1109/AVSS.2019.8909899
Liu, W., et al.: SSD: single shot multibox detector. arXiv:1512.02325 [cs], 9905:21–37 (2016). https://doi.org/10.1007/978-3-319-46448-02
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv:1506.01497 [cs], January 2016
Sai, B.N.K., Sasikala, T.: Object detection and count of objects in image using tensor flow object detection API. In: 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 542–546 (2019). https://doi.org/10.1109/ICSSIT46314.2019.8987942
Shao, S., et al.: Objects365: a large-scale, high-quality dataset for object detection. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 8429–8438, Seoul, Korea (South), October 2019. IEEE. ISBN 978-1-72814-803-8. https://doi.org/10.1109/ICCV.2019.00852
Statista, KI: Relevante Technologien in Mittelstandsunternehmen. www.de.statista.com/statistik/daten/studie/1297723/umfrage/relevante- technologien-kuenstlicher-intelligenz-in-mittelstandsunternehmen/. Accessed 18 Nov 2022
Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection. arXiv:1911.09070 [cs, eess], July 2020
Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, vol. 1, pp. 29–39, April 2000
Zou, Z., Shi, Z., Guo, Y., Ye, J.: object detection in 20 years: a survey. arXiv:1905.05055 [cs], May 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Stecker, B., Brandt-Pook, H. (2023). Application of Object Detection Models for the Detection of Kitchen Furniture - A Comparison. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14126. Springer, Cham. https://doi.org/10.1007/978-3-031-42508-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-42508-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42507-3
Online ISBN: 978-3-031-42508-0
eBook Packages: Computer ScienceComputer Science (R0)