Abstract
This study introduces EA-OBB, a lightweight rotated object detection framework designed for detecting one-dimensional (1D) and Quick Response barcodes. Built upon the YOLO11 architecture, EA-OBB integrates several innovative modules-KWConv, ORPNCSPELAN, and LADH-OBB-to enhance both accuracy and computational efficiency in rotated object detection. The KWConv module utilizes a dynamic convolution kernel mechanism to improve rotational barcode feature extraction. The ORPNCSPELAN module enhances computational efficiency through multi-path feature aggregation and online re-parameterization. The LADH-OBB module decouples classification and regression tasks, improving the precision of rotation angle regression. To further adapt to resource-constrained environments, this study incorporates the Taylor Pruning algorithm, significantly reducing model parameters and computational costs. Experimental results on the RotBar dataset demonstrate the superior performance of EA-OBB, achieving an optimal balance of precision, recall, and computational complexity compared to existing methods.
Similar content being viewed by others
Data availability
The data presented in this study are available on request from the corresponding author.
References
Ding, Y., Jin, M., Li, S. & Feng, D. Smart logistics based on the internet of things technology: An overview. Int. J. Logist. Res. Appl.24, 323–345 (2021).
Yan, L.-Y., Tan, G.W.-H., Loh, X.-M., Hew, J.-J. & Ooi, K.-B. Qr code and mobile payment: The disruptive forces in retail. J. Retail. Consum. Serv.58, 102300 (2021).
De Luna, I. R., Liébana-Cabanillas, F., Sánchez-Fernández, J. & Muñoz-Leiva, F. Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technol. Forecasting Soc. Change146, 931–944 (2019).
Reyes Ruiz, G. Product and content management through qr codes as an efficient strategy in e-commerce. In Algorithms and Computational Techniques Applied to Industry, 365–390 (Springer, 2022).
Sörös, G. Gpu-accelerated joint 1d and 2d barcode localization on smartphones. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5095–5099 (IEEE, 2014).
Katona, M. & Nyúl, L. G. Efficient 1d and 2d barcode detection using mathematical morphology. In Mathematical Morphology and Its Applications to Signal and Image Processing: 11th International Symposium, ISMM 2013, Uppsala, Sweden, May 27-29, 2013. Proceedings 11, 464–475 (Springer, 2013).
Tizhoosh, H. R. & Rahnamayan, S. Evolutionary projection selection for radon barcodes. In 2016 IEEE Congress on Evolutionary Computation (CEC), 1–8, https://doi.org/10.1109/CEC.2016.7743771 (2016).
LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE86, 2278–2324 (1998).
Ren, S., He, K., Girshick, R. & Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transac. Patt. Anal. Mach. Intell.39, 1137–1149 (2016).
Liu, W. et al. Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, 21–37 (Springer, 2016).
Redmon, J. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (2016).
Molchanov, P., Mallya, A., Tyree, S., Frosio, I. & Kautz, J. Importance estimation for neural network pruning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 11264–11272 (2019).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst.25 (2012).
Carion, N. et al. End-to-end object detection with transformers. In European conference on computer vision, 213–229 (Springer, 2020).
Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, 234–241 (Springer, 2015).
Tekin, B., Sinha, S. N. & Fua, P. Real-time seamless single shot 6d object pose prediction. In Proceedings of the IEEE conference on computer vision and pattern recognition, 292–301 (2018).
Zhu, Y. & Tang, H. Automatic damage detection and diagnosis for hydraulic structures using drones and artificial intelligence techniques. Remote Sensing15, 615 (2023).
Nie, X., Peng, J., Wu, Y., Gupta, B. B. & Abd El-Latif, A. A. Real-time traffic speed estimation for smart cities with spatial temporal data: A gated graph attention network approach. Big Data Res.28, 100313 (2022).
Jha, M., Gupta, R. & Saxena, R. A framework for in-vivo human brain tumor detection using image augmentation and hybrid features. Health Inf. Sci. Syst.10, 23 (2022).
Girshick, R., Donahue, J., Darrell, T. & Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 580–587 (2014).
Girshick, R. Fast r-cnn. arXiv preprint arXiv:1504.08083 (2015).
He, K., Gkioxari, G., Dollár, P. & Girshick, R. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, 2961–2969 (2017).
Cai, Z. & Vasconcelos, N. Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, 6154–6162 (2018).
Terven, J., Córdova-Esparza, D.-M. & Romero-González, J.-A. A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas. Mach. Learn. Knowl. Extr.5, 1680–1716 (2023).
Redmon, J. & Farhadi, A. Yolo9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition, 7263–7271 (2017).
Farhadi, A., Redmon, J. & Yolov3: An incremental improvement. In Computer vision and pattern recognition, vol.,. 1–6 (Springer 2018 (Berlin/Heidelberg, Germany, 1804).
Bochkovskiy, A., Wang, C.-Y. & Liao, H.-Y. M. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020).
Jocher, G. YOLOv5 by Ultralytics, https://doi.org/10.5281/zenodo.3908559 (2020).
Ge, Z. Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021).
Li, C. et al. Yolov6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976 (2022).
Wang, C.-Y., Bochkovskiy, A. & Liao, H.-Y. M. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 7464–7475 (2023).
Jocher, G., Chaurasia, A. & Qiu, J. Ultralytics YOLO (2023).
Wang, C.-Y., Yeh, I.-H. & Mark Liao, H.-Y. Yolov9: Learning what you want to learn using programmable gradient information. In European conference on computer vision, 1–21 (Springer, 2025).
Wang, A. et al. Yolov10: Real-time end-to-end object detection. arXiv preprint arXiv:2405.14458 (2024).
Hansen, D. K., Nasrollahi, K., Rasmussen, C. B. & Moeslund, T. B. Real-time barcode detection and classification using deep learning. In International Joint Conference on Computational Intelligence, 321–327 (SCITEPRESS Digital Library, 2017).
Kalinov, I. et al. Warevision: Cnn barcode detection-based uav trajectory optimization for autonomous warehouse stocktaking. IEEE Robot. Autom. Lett.5, 6647–6653 (2020).
Karrach, L., Pivarčiová, E. & Božek, P. Identification of Qr code perspective distortion based on edge directions and edge projections analysis. J. Imaging6, 67 (2020).
Chen, R. et al. Rapid detection of multi-Qr codes based on multistage stepwise discrimination and a compressed mobilenet. IEEE Internet Things J.10, 15966–15979 (2023).
Alaca, Y. & Çelik, Y. Cyber attack detection with Qr code images using lightweight deep learning models. Comput. Secur.126, 103065 (2023).
Zhao, L. et al. Yolov8-Qr: An improved yolov8 model via attention mechanism for object detection of Qr code defects. Comput. Electr. Eng.118, 109376 (2024).
Keshun, Y., Yingkui, G., Yanghui, L. & Yajun, W. A novel physical constraint-guided quadratic neural networks for interpretable bearing fault diagnosis under zero-fault sample. Nondestructive Testing and Evaluation 1–31 (2025).
Chen, Z. et al. Railvoxeldet: A lightweight 3-d object detection method for railway transportation driven by onboard lidar data. IEEE Internet Things J.12, 37175–37189. https://doi.org/10.1109/JIOT.2025.3582636 (2025).
Chen, Z. et al. Foreign object detection method for railway catenary based on a scarce image generation model and lightweight perception architecture. IEEE Transac. Circ. Syst. Video Technol. https://doi.org/10.1109/TCSVT.2025.3567319 (2025).
Kim, J. H. et al. Distilling and refining domain-specific knowledge for semi-supervised domain adaptation. In BMVC, 606 (2022).
Ngo, B. H., Do-Tran, N.-T., Nguyen, T.-N., Jeon, H.-G. & Choi, T. J. Learning cnn on vit: A hybrid model to explicitly class-specific boundaries for domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 28545–28554 (2024).
Hu, M. et al. Online convolutional re-parameterization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 568–577 (2022).
Li, C. & Yao, A. Kernelwarehouse: Rethinking the design of dynamic convolution. arXiv preprint arXiv:2406.07879 (2024).
Ding, X. et al. Repvgg: Making vgg-style convnets great again. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13733–13742 (2021).
Zhang, J., Chen, Z., Yan, G., Wang, Y. & Hu, B. Faster and lightweight: An improved yolov5 object detector for remote sensing images. Remote Sensing15, 4974 (2023).
Yang, S., Pei, Z., Zhou, F. & Wang, G. Rotated faster r-cnn for oriented object detection in aerial images. In Proceedings of the 2020 3rd International Conference on Robot Systems and Applications, 35–39 (2020).
Lin, T. Focal loss for dense object detection. arXiv preprint arXiv:1708.02002 (2017).
Yang, Z., Liu, S., Hu, H., Wang, L. & Lin, S. Reppoints: Point set representation for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, 9657–9666 (2019).
Tian, Z., Shen, C., Chen, H. & He, T. Fcos: Fully convolutional one-stage object detection. arxiv 2019. arXiv preprint arXiv:1904.01355 (2019).
Xu, Y. et al. Gliding vertex on the horizontal bounding box for multi-oriented object detection. IEEE Transac. Patt. Anal. Mach. Intell.43, 1452–1459 (2020).
Zhang, S., Chi, C., Yao, Y., Lei, Z. & Li, S. Z. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 9759–9768 (2020).
Yang, X., Yan, J., Feng, Z. & He, T. R3det: Refined single-stage detector with feature refinement for rotating object. In Proceedings of the AAAI conference on artificial intelligence35, 3163–3171 (2021).
Han, J., Ding, J., Li, J. & Xia, G.-S. Align deep features for oriented object detection. IEEE Transac. Geosci. Remote Sensing60, 1–11 (2021).
Xie, X., Cheng, G., Wang, J., Yao, X. & Han, J. Oriented r-cnn for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, 3520–3529 (2021).
Hou, L., Lu, K., Xue, J. & Li, Y. Shape-adaptive selection and measurement for oriented object detection. In Proceedings of the AAAI Conference on Artificial Intelligence36, 923–932 (2022).
Funding
This research was funded by the Shenzhen Science and Technology Program by grant No. 20231128141501001 and NO. JCYJ20250604145033043, and the Natural Science Foundation of Top Talent of SZTU by grant No. GDRC202415.
Author information
Authors and Affiliations
Contributions
The authors confirm contribution to the paper as follows: Conceptualization: P.L., Z.M., Q.W., T.Z., and X.S.; Methodology: P.L. and Z.M.; Software: P.L. and Z.M.; Validation: Q.W., T.Z., and X.S.; Formal analysis: P.L., Z.M., Q.W., T.Z., and X.S.; Investigation: P.L., Z.M., Q.W., T.Z., and X.S.; Resources: Q.W., T.Z., and X.S.; Data curation: P.L., Z.M., Q.W., T.Z., and X.S.; Writing-original draft: P.L. and Z.M.; Writing-review & editing: P.L., Z.M., Q.W., T.Z., and X.S.; Visualization: Q.W., T.Z., and X.S.; Supervision: Q.W., T.Z., and X.S.; Project administration: Q.W., T.Z., and X.S.; Funding acquisition: Q.W., T.Z., and X.S. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Luo, P., Ma, Z., Wu, Q. et al. Efficient adaptive rotated object detection for 1D and QR barcodes. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34854-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-025-34854-y
