Lin, H.; Hsieh, P.; Chou, S.; Chang, C. An Improved Ensemble Learning-Based Approach for Retail Product Recognition. Preprints2023, 2023111141. https://doi.org/10.20944/preprints202311.1141.v1
APA Style
Lin, H., Hsieh, P., Chou, S., & Chang, C. (2023). An Improved Ensemble Learning-Based Approach for Retail Product Recognition. Preprints. https://doi.org/10.20944/preprints202311.1141.v1
Chicago/Turabian Style
Lin, H., Sen-Yih Chou and Chin-Chen Chang. 2023 "An Improved Ensemble Learning-Based Approach for Retail Product Recognition" Preprints. https://doi.org/10.20944/preprints202311.1141.v1
Abstract
Due to the recent trend of unmanned economy, retail stores have gradually reduced their service and cashier manpower. The retail product recognition becomes one of key issues for unmanned shopping. Although the success of deep neural networks has made object recognition feasible in a variety of applications, it still struggles to perform well on a large number of classes of retail products. In this paper, we propose an improved ensemble learning-based approach for retail product recognition. In the proposed approach, the object classification network is first improved through feature extraction and block attention. An ensemble model is then built by integrating multiple network models and using loss selection as model weights. In the experiments, the feasibility of our ensemble learning-based approach has been evaluated through many production items. The results demonstrate the effectiveness of the proposed approach compared with previous retail product recognition methods.
Keywords
ensemble learning; retail product recognition; convolutional neural network; unmanned store
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
