REFERENCES
1. Lusiani, M.; Zulkarnain; Komarudin. Object detection in container terminals based on deep learning approach: a systematic literature review. In 2024 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bangkok, Thailand. Dec 15-18, 2024. IEEE; 2024. pp. 0560-5.
2. Liu, Y.; Chen, H. A lightweight container detection network model based on YOLOv5s. In 2023 42nd Chinese Control Conference (CCC), Tianjin, China. Jul 24-26, 2023. IEEE; 2023. pp. 4267-72.
3. Pambudi, D. S.; Handayani, R.; Hidayah, L. Template matching algorithm for noise detection in cargo container. In 2018 Third International Conference on Informatics and Computing (ICIC), Palembang, Indonesia. Oct 17-18, 2018. IEEE; 2018. p. 1-9.
4. İmamoğlu, Z. E.; Tuğlular, T.; Baştanlar, Y. Container damage detection and classification using container images. In 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey. Oct 05-07, 2020. IEEE; 2020. p. 1-4.
5. Huang, X. R.; Huang, G. Z.; Kuo, S. Y.; Chen, L. B. A vision transformer model-based shipping container damage inspection scheme. In 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), Taichung, Taiwan. Jul 09-11, 2024. IEEE; 2024. pp. 817-18.
6. Kuo, S. Y.; Lin, P. C.; Huang, X. R.; Huang, G. Z.; Chen, L. B. Cad-Transformer: a CNN–transformer hybrid framework for automatic appearance defect classification of shipping containers. IEEE. Trans. Instrum. Meas. 2025, 74, 1-21.
7. Huang, Y.; Chen, H.; Zheng, Z.; Jia, Y.; Chen, G. Attention-enhanced YOLO-based framework for container damage detection in port environments. In 2026 5th International Symposium on Computer Applications and Information Technology (ISCAIT), Dongguan, China. Jan 23-25, 2026. IEEE; 2026. pp. 493-7.
8. Zhao, Z. Research on port container damage detection algorithm based on C2PSA-improved YOLOv11. In 2026 5th International Symposium on Computer Applications and Information Technology (ISCAIT), Dongguan, China. Jan 23-25, 2026. IEEE; 2026; pp. 79-83.
9. Zheng, Y.; Shi, X.; Zhou, Y.; Hu, K.; Li, W. Container damage detection method based on improved YOLOv8. In 2025 6th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Shanghai, China. Oct 17-19, 2025. IEEE; 2025. pp. 220-3.
10. Bahrami, Z.; Zhang, R.; Rayhana, R.; Wang, T.; Liu, Z. Optimized deep neural network architectures with anchor box optimization for shipping container corrosion inspection. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia. Dec 01-04, 2020. IEEE; 2020. pp. 1328-33.
11. Nguyen Thi Phuong, T.; Cho, G. S.; Chatterjee, I. Automating container damage detection with the YOLO-NAS deep learning model. Sci. Prog. 2025, 108, 00368504251314084.
12. Zhang, L. Container damage detection based on self contrastive learning and deep convolutional neural networks. In 2025 5th International Conference on Electronic Communication, Computer Science and Technology (ECCST), Qinhuangdao, China. Dec 26-28, 2025. IEEE; 2025. pp. 221-5.
13. Vasileiadis, S.; Aslam, S.; Orphanides, K.; et al. Real-time container tracking and damage detection at seaports using deep learning. In 20th Conference on Computer Science and Intelligence Systems (FedCSIS), Kraków, Poland. Sep 14-17, 2025. IEEE; 2025. pp. 277-85.
14. Rahman, A.; Khan, M. S. H.; Tazwar, M.; Rahat, M. T.; Nur, K. AutoContDamage: automated container damage detection in ports using RF-DETR model for enhanced operational efficiency. In 2025 28th International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar, Bangladesh. Dec 19-21, 2025. IEEE; 2025. pp. 2986-91.
15. Li, X.; Huang, X.; Liu, Q. Container damage identification based on RP-FCN. In 2020 39th Chinese Control Conference (CCC), Shenyang, China. Jul 27-29, 2020. IEEE; 2020. pp. 7031-4.
16. Wang, H.; Liu, Q.; Zhang, G. Container damage detection algorithm based on fast-solo. In Proceedings of 2022 Chinese Intelligent Systems Conference. Jia, Y.; Zhang, W.; Fu, Y.; Zhao, S.; Eds; Singapore: Springer Nature Singapore; 2022. pp. 119-31.
17. Su, W.; Yu, N. An end-to-end detection and segmentation model for container surface damage based on YOLOv11. In 2025 5th International Conference on Electronic Information Engineering and Computer Communication (EIECC), Wuhan, China. Dec 26-28, 2025. IEEE; 2025. pp. 928-32.
18. Ornelas, D.; Canedo, D.; Neves, A. J. R. Semantic segmentation of corrosion in cargo containers using deep learning. Sustainability 2025, 17, 6480.
19. Liu, Z.; Zhao, W.; Jia, N.; Liu, X.; Yang, J. SANet: scale-adaptive network for lightweight salient object detection. Intell. Robot. 2024, 4, 503-23.
20. Božič, J.; Tabernik, D.; Skočaj, D. Mixed supervision for surface-defect detection: from weakly to fully supervised learning. Comput. Ind. 2021, 129, 103459.
21. He, K.; Gkioxari, G.; Dollár, P.; Girshick, R. Mask R-CNN. In 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy. Oct 22-29, 2017. IEEE; 2017. pp. 2980-8.
22. Xu, D.; Ouyang, W.; Wang, X.; Sebe, N. PAD-Net: multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA. Jun 18-23, 2018. IEEE; 2018. pp. 675-84.
23. Tian, Y.; Ye, Q.; Doermann, D. YOLOv12: attention-centric real-time object detectors. In Proceedings of the 39th International Conference on Neural Information Processing Systems, San Diego, USA and Mexico City, Mexico. Curran Associates Inc.; 2025. pp. 78433-57.
24. Woo, S.; Park, J.; Lee, J. Y.; Kweon, I. S. CBAM: convolutional block attention module. In Computer Vision - ECCV 2018. Cham: Springer International Publishing; Ferrari, V.; Hebert, M.; Sminchisescu, C.; Weiss, Y.; Eds; Springer, Cham; 2018. pp. 3-19.
25. COCO API: Python Detection Evaluation (cocoeval.py). https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py. (accessed 2026-10-23).
26. Cheng, B.; Girshick, R.; Dollár, P.; Berg, A. C.; Kirillov, A. Boundary IoU: improving object-centric image segmentation evaluation. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA. Jun 20-25, 2021. IEEE; 2021; pp. 15329-37.
27. Wang, A.; Chen, H.; Liu, L.; et al. YOLOv10: real-time end-to-end object detection. In Proceedings of the 38th International Conference on Neural Information Processing Systems, Vancouver, Canada. Curran Associates Inc.; 2024. pp. 107984-8011.
28. Jocher, G.; Qiu, J. Ultralytics YOLO11. Version 11.0.0; 2024. https://github.com/ultralytics/ultralytics. (accessed 2026-10-23).
29. Lei, M.; Li, S.; Wu, Y.; et al. YOLOv13: real-time object detection with hypergraph-enhanced adaptive visual perception. arXiv 2025, arXiv:2506.17733. Available online: https://doi.org/10.48550/arXiv.2506.17733. (accessed 2026-10-23).
30. Jocher, G.; Qiu, J.; Liu, M.; et al. Ultralytics YOLO26: unified real-time end-to-end vision models. arXiv 2026, arXiv:2606.03748. Available online: https://doi.org/10.48550/arXiv.2606.03748. (accessed 2026-10-23).
31. Zhao, Y.; Lv, W.; Xu, S.; et al. DETRs beat YOLOs on real-time object detection. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA. Jun 16-22, 2024. IEEE; 2024; pp. 16965-74.
32. Lv, W.; Zhao, Y.; Chang, Q.; Huang, K.; Wang, G.; Liu, Y. RT-DETRv2: improved baseline with bag-of-freebies for real-time detection transformer. arXiv 2024, arXiv:2407.17140. Available online: https://doi.org/10.48550/arXiv.2407.17140. (accessed 2026-10-23).
33. Peng, Y.; Li, H.; Wu, P.; Zhang, Y.; Sun, X.; Wu, F. D-FINE: redefine regression task of DETRs as fine-grained distribution refinement. In International Conference on Learning Representations; 2025. pp. 44015-31. https://proceedings.iclr.cc/paper_files/paper/2025/file/6cf58a87e3097e7d1f9be3e8693a93de-Paper-Conference.pdf. (accessed 2026-10-23).
34. Huang, S.; Lu, Z.; Cun, X.; Yu, Y.; Zhou, X.; Shen, X. DEIM: DETR with improved matching for fast convergence. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA. Jun 10-17, 2025. IEEE; 2025. pp. 15162-71.
35. Robinson, I.; Robicheaux, P.; Popov, M.; Ramanan, D.; Peri, N. RF-DETR: neural architecture search for real-time detection trans-formers. In International Conference on Learning Representations; 2026. https://proceedings.iclr.cc/paper_files/paper/2026/file/5bed8703db85ab27dc32f6a42f8fbdb6-Paper-Conference.pdf. (accessed 2026-10-23).
