Object Detection for Retail Products with TensorFlow 2
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Published:
Dec 30, 2024
Keywords:
TensorFlow, Object Detection, Mobile Phone, On-Shelf Availability
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Abstract
On-shelf availability is a crucial aspect in the retail industry, directly impacting customer satisfaction and sales. Artificial intelligence-based object detection technology can enhance efficiency in monitoring product availability. This study examines the implementation of TensorFlow 2 for detecting retail products on shelves, using the SSD MobileNetV2 FPNLite architecture. Three model variations were developed based on input image sizes: 320x320, 640x640, and 1024x1024. The models were trained using transfer learning with a dataset containing 128 retail product classes. Evaluation results show that the 640x640 model achieved the best performance in terms of the trade-off between precision and speed, with a mAP of 0.72049 and an inference time of 0.283 seconds. The 320x320 model had the fastest inference time of 0.073 seconds, making it suitable for real-time applications. This study offers a solution to improve retail stock management through automatic object detection, aiming to reduce the risk of out-of-stock situations.
Article Details
How to Cite
Ahmad Azzam Alhanafi, & Arrie Kurniawardhani. (2024). Object Detection for Retail Products with TensorFlow 2. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 5(4), 144 - 152. https://doi.org/10.62527/jitsi.5.4.298
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References
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JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, Volume x No x, March xxxx Hal x - xx
Ahmad Azzam Alhanafi, Arrie Kurniawardhani 11Deteksi Objek untuk Produk Retail dengan TensorFlow 2
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[1] Har, Loh & Rashid, Umi Kartini & Te Chuan, Lee & Yin Xia, Loh. (2022). Revolution of Retail
Industry: From Perspective of Retail 1.0 to 4.0. Procedia Computer Science. 200.
10.1016/j.procs.2022.01.362.
[2] Klimchenia, L.. (2024). Retail transformation concepts development under the influence of industrial
revolutions. Vestnik Universiteta. 121-129. 10.26425/1816-4277-2024-6-121-129.
[3] Rai, Prakruthi & Nanjundan, Preethi & George, Jossy. (2024). Enhancing Industrial Operations through
AI-Driven Decision-Making in the Era of Industry 4.0. 10.1201/9781003432319-3..
[4] Andaur, Juan & Ruz, Gonzalo & Goycoolea, Marcos. (2021). Predicting Out-of-Stock Using Machine
Learning: An Application in a Retail Packaged Foods Manufacturing Company. Electronics. 10. 2787.
10.3390/electronics10222787.
[5] Šikić F, Kalafatić Z, Subašić M, Lončarić S. (2024). Enhanced Out-of-Stock Detection in Retail Shelf
Images Based on Deep Learning. Sensors; 24(2):693. https://doi.org/10.3390/s2402069.
[6] Afram, G. (2020). A study on how to improve On-shelf availability : With deep learning (Dissertation).
Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-40006.
[7] al Azzo, Fadwa & Taqi, Arwa & Milanova, Mariofanna. (2018). Human Related-Health Actions
Detection using Android Camera based on TensorFlow Object Detection API. International Journal of
Advanced Computer Science and Applications. 9. 10.14569/IJACSA.2018.091002.
[8] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J.,
Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Kaiser, L., Kudlur, M.,
Levenberg, J., & Zheng, X. (2015). TensorFlow : Large-Scale Machine Learning on Heterogeneous
Distributed Systems. arXiv:1603.04467v2 [cs.DC]
[9] Vasilakopoulos, G. P., (2024). An Automatic Differentiation Algorithm for Recursive Functions and its
Implementation in TensorFlow. Thesis. National and Kapodistrian University of Athens.
[10] Rezaei Nasab, Ali & Dashti, Maedeh & Shahin, Mojtaba & Zahedi, Mansooreh & Khalajzadeh, Hourieh
& Arora, Chetan & Liang, Peng. (2024). Fairness Concerns in App Reviews: A Study on AI-based
Mobile Apps. 10.48550/arXiv.2401.08097.
[11] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single
Shot Multibox Detector. European Conference on Computer Vision (ECCV), 21-37.
[12] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted
Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 4510-4520.
[13] Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H.
(2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv
preprint arXiv:1704.04861.
[14] Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature Pyramid
Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 2117-2125.
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, Volume x No x, March xxxx Hal x - xx
Ahmad Azzam Alhanafi, Arrie Kurniawardhani 11Deteksi Objek untuk Produk Retail dengan TensorFlow 2
[15] Goldsborough, P. (2016). A Tour of TensorFlow. ArXiv, abs/1610.01178.
[16] LabelImg, "LabelImg: LabelImg is a graphical image annotation tool and label object bounding boxes in
images", Available: https://github.com/heartexlabs/labelImg, accessed on 22 October 2024.
[17] Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010). The Pascal Visual
Object Classes (VOC) Challenge. International Journal of Computer Vision, 88(2), 303–338.
https://doi.org/10.1007/s11263-009-0275-4..
[18] TensorFlow, "TFRecord and tf.train.Example", Available:
https://www.tensorflow.org/tutorials/load_data/tfrecord, accessed on 22 October 2024.
[19] TensorFlow, "TensorFlow Object Detection API," Available:
https://github.com/tensorflow/models/tree/master/research/object_detection, accessed on 22 October
2024.
[20] TensorFlow, "Training a TensorFlow Object Detection Model" Available:
https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html, accessed on 22
Oktober 2024.
[21] Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., & Zitnick, C. L. (2014).
Microsoft COCO: Common Objects in Context. European Conference on Computer Vision (ECCV),
740–755. https://doi.org/10.1007/978-3-319-10602-1_48.
[22] Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge
University Press.
[23] TensorFlow, "TensorFlow Lite", Available: https://www.tensorflow.org/lite, accessed on 23 October
2024.