Automating Image Quality Detection for Parcel Identification Systems: A Computer Vision Approach

DOI: https://doi.org/10.62517/jbdc.202401314

Author(s)

Qiuyu Tian1,*, Kun Wang2, Hongwei Tang1,2,3

Affiliation(s)

1Institute of Information Superbahn, Nanjing, China 2Institute of Computing Technology Chinese Academy of Sciences, Beijing, China 3University of Chinese Academy of Sciences, Nanjing, China *Corresponding Author.

Abstract

This paper presents an overview of the computer vision methodologies employed in an image quality detection project for parcel identification systems. The core objective of this project is to minimize erroneous chargebacks that occur when the system fails to capture accurate carton label information. Our developed defect detection framework replaces the labor-intensive and error-prone human validation process, thereby significantly reducing labor costs and improving the accuracy of defect identification in parcel identification images. The system uses a multi-sided vision tunnel to capture images of cartons from all angles. However, various issues such as dirty cameras, out-of-focus images, overly bright camera flashes, and partial images can impair image quality, leading to failures in automated parcel receiving. Each failure incurs additional costs for the logistics provider and may result in unwarranted chargebacks to partners, adversely affecting relationships. To address these challenges, we propose a computer vision-based approach to systematically identify and exclude poor-quality images from chargeback datasets, preventing erroneous chargebacks. This approach not only enhances the accuracy of automated receiving operations, but also supports downstream analyses to identify locations with frequent image quality issues. By partnering with these locations, preventive measures can be implemented to improve parcel image quality across the network. The outcomes of this project aim to streamline the automated receiving process, reduce operational errors, and foster better partner relationships by ensuring fair and accurate chargeback practices.

Keywords

Auto-receive; Image Quality; Vendor Chargeback; Computer Vision

References

[1]Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 (pp. 21-37). Springer International Publishing. [2]Pinheiro Cinelli, L., Araújo Marins, M., Barros da Silva, E. A., & Lima Netto, S. (2021). Variational autoencoder. In Variational Methods for Machine Learning with Applications to Deep Networks (pp. 111-149). Cham: Springer International Publishing. [3]Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems, 33(12), 6999-7019. [4]Ross, Girshick. "Rich feature hierarchies for accurate object detection and semantic segmentation". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. pp. 580–587. arXiv: 1311.2524. doi: 10.1109/CVPR.2014.81. ISBN 978-1-4799-5118-5. S2CID 215827080 (2014). [5]Ren, S., He, K., Girshick, R., & Sun, J. (2016). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 39(6), 1137-1149. [6]Redmon, J. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. [7]He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). [8]Liu, S., & Deng, W. (2015, November). Very deep convolutional neural network based image classification using small training sample size. In 2015 3rd IAPR Asian conference on pattern recognition (ACPR) (pp. 730-734). IEEE. [9]Hosmer, David W.; Lemeshow, Stanley. Applied Logistic Regression (2nd ed.). Wiley. ISBN 978-0-471-35632-5. (2000) [10]Cortes, Corinna; Vapnik, Vladimir. "Support-vector networks" (PDF). Machine Learning. 20 (3): 273–297. CiteSeerX 10.1.1.15.9362. doi:10.1007/BF00994018. S2CID 206787478. (1995) [11]Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27. [12]Webb, Geoffrey I., Eamonn Keogh, and Risto Miikkulainen. "Naïve Bayes." Encyclopedia of machine learning 15.1 (2010): 713-714. [13]Powers, David M W. "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation" (PDF). Journal of Machine Learning Technologies. 2 (1): 37–63. (2011).