Skip to main content

Advertisement

Highway spillage detection using an improved STPM anomaly detection network from a surveillance perspective

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Spillages may cause traffic congestion and incidents and seriously affect the efficiency of traffic operation. Due to the changeable shape and scale of a spill on a highway, the location of the spill is random, so the current background extraction and object detection methods cannot achieve good detection results for the spill. This paper proposes a highway spill detection method using an improved STPM anomaly detection network. The method is based on the STPM network and achieves detection through FFDNet image filtering, calculation of the global correlation features of the student and teacher networks, contour positioning of spillages in the feature map, and automatic collection of positive samples to train and update the model, achieving high-precision identification and positioning of the spillages. The experimental results of the custom-built top-view road surface spillage dataset and the MVTec anomaly detection dataset show that the method proposed in this paper can obtain an AOC-ROC value of 0.978 and a PRO score of 0.965 and can distinguish between spillages and reflective cones, avoiding the problem of false detection when spills are similar in appearance. Therefore, the proposed method has value in the research and engineering application of spill detection in special highway scenes.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability statements

Some datasets analysed during the current study are not publicly available due privacy reasons. Our code is based on modifications to the STPM network code. Due to privacy concerns regarding the code and data, we are able to provide the original code for the STPM network we utilized, which can be found at this link: https://github.com/xiahaifeng1995/STPM-Anomaly-Detection-Localization-master. Some data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Liang H, Song H, Yun X et al (2022) Traffic incident detection based on a global trajectory spatiotemporal map. Complex & Intelligent Systems pp 1–20

  2. Moriano P, Berres A, Xu H et al (2024) Spatiotemporal features of traffic help reduce automatic accident detection time. Expert Syst Appl 244:122813

    Article  MATH  Google Scholar 

  3. Wang J, Liu B, Fu T et al (2019) Modeling when and where a secondary accident occurs. Accid Anal Prev 130:160–166

    Article  MATH  Google Scholar 

  4. Pramanik A, Sarkar S, Maiti J (2021) A real-time video surveillance system for traffic pre-events detection. Accid Anal Prev 154:106019

    Article  MATH  Google Scholar 

  5. Nayak R, Pati UC, Das SK (2021) A comprehensive review on deep learning-based methods for video anomaly detection. Image Vis Comput 106:104078

    Article  MATH  Google Scholar 

  6. Yan S, Chen P, Chen H et al (2024) Multiresolution feature guidance based transformer for anomaly detection. Appl Intell 54(2):1831–1846

    Article  MATH  Google Scholar 

  7. Patel AS, Merlino G, Bruneo D et al (2021) Video representation and suspicious event detection using semantic technologies. Semantic Web 12(3):467–491

    Article  MATH  Google Scholar 

  8. Sathesh A, Hamdan YB (2021) Speedy detection module for abandoned belongings in airport using improved image processing technique. J Trends Comput Sci Smart Technol 3(4):251

    Article  MATH  Google Scholar 

  9. Din M, Bashir A, Basit A et al (2020) Abandoned object detection using frame differencing and background subtraction. Int J Adv Comput Sci Appl 11(7)

  10. Park H, Park S, Joo Y (2020) Robust real-time detection of abandoned objects using a dual background model. KSII Trans Internet Inf Syst (TIIS) 14(2):771–788

    MATH  Google Scholar 

  11. Su H, Wang W, Wang S (2023) A robust all-weather abandoned objects detection algorithm based on dual background and gradient operator. Multimed Tools Appl 82(19):29477–29499

    Article  MATH  Google Scholar 

  12. Boukhriss RR, Fendri E, Hammami M (2020) Moving object detection under different weather conditions using full-spectrum light sources. Pattern Recogn Lett 129:205–212

    Article  Google Scholar 

  13. Russel NS, Selvaraj A (2023) Ownership of abandoned object detection by integrating carried object recognition and context sensing. The Visual Computer pp 1–26

  14. An Y, Zhao X, Yu T, et al (2023) Zbs: Zero-shot background subtraction via instance-level background modeling and foreground selection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 6355–6364

  15. Dwivedi N, Singh DK, Kushwaha DS (2020) An approach for unattended object detection through contour formation using background subtraction. Procedia Comput Sci 171:1979–1988

  16. Teja Y (2023) Static object detection for video surveillance. Multimed Tools Appl 82(14):21627–21639

    Article  MATH  Google Scholar 

  17. Ahammed MT, Ghosh S, Ashik MAR (2022) Human and object detection using machine learning algorithm. In: 2022 Trends in electrical, electronics, computer engineering conference (TEECCON), IEEE, pp 39–44

  18. Dogariu M, Stefan LD, Constantin MG et al (2020) Human-object interaction: application to abandoned luggage detection in video surveillance scenarios. In: 2020 13th International Conference on Communications (COMM), IEEE, pp 157–160

  19. Weliwita W, Isuru J, Premaratne S (2021) Modeling abandoned object detection and recognition in real-time surveillance. Int J Eng Trends Technol 69(2):188–193

    Article  Google Scholar 

  20. Wang Y, Zhai J (2023) Highway abandoned object detection based on foreground extraction. In: Chinese Intelligent Systems Conference, Springer, pp 367–376

  21. Lwin SP, Tun MT (2022) Deep convonlutional neural network for abandoned object detection. Int Res J Mod Eng Technol Sci 4(01):1549–1553

    MATH  Google Scholar 

  22. Li F, Jiang Z, Zhou S et al (2022) Spilled load detection based on lightweight yolov4 trained with easily accessible synthetic dataset. Comput Electr Eng 100:107944

    Article  Google Scholar 

  23. Zhou S, Bi Y, Wei X et al (2021) Automated detection and classification of spilled loads on freeways based on improved yolo network. Mach Vis Appl 32:1–12

    Article  MATH  Google Scholar 

  24. Preetha K et al (2021) A fuzzy rule-based abandoned object detection using image fusion for intelligent video surveillance systems. Turk J Comput Math Educ (TURCOMAT) 12(3):3694–3702

  25. Lamar Leon J, Alonso Baryolo R, Garcia Reyes E et al (2023) Abandoned object detection using persistent homology. In: Iberoamerican congress on pattern recognition. Springer, pp 178–188

  26. Asad M, Jiang H, Yang J et al (2022) Multi-stream 3d latent feature clustering for abnormality detection in videos. Appl Intell 52(1):1126–1143

  27. Huaiyu C, Zhaoqian Y, Ziyang C et al (2024) Image-guided and point cloud space-constrained method for detection and localization of abandoned objects on the road. Opto-Electron Eng 51(3):230317

  28. Sun C, Jia Y, Song H et al (2020) Adversarial 3d convolutional auto-encoder for abnormal event detection in videos. IEEE Trans Multimedia 23:3292–3305

    Article  MATH  Google Scholar 

  29. Caetano F, Carvalho P, Cardoso J (2022) Deep anomaly detection for in-vehicle monitoring–an application-oriented review. Appl Sci 12(19):10011

    Article  MATH  Google Scholar 

  30. Lee S, Kim HG, Ro YM (2019) Bman: Bidirectional multi-scale aggregation networks for abnormal event detection. IEEE Trans Image Process 29:2395–2408

    Article  MATH  Google Scholar 

  31. Wang X, Che Z, Jiang B et al (2021) Robust unsupervised video anomaly detection by multipath frame prediction. IEEE Trans Neural Netw Learn Syst 33(6):2301–2312

    Article  MathSciNet  MATH  Google Scholar 

  32. Le VT, Kim YG (2023) Attention-based residual autoencoder for video anomaly detection. Appl Intell 53(3):3240–3254

    Article  MATH  Google Scholar 

  33. Cohen N, Hoshen Y (2020) Sub-image anomaly detection with deep pyramid correspondences. arXiv:2005.02357

  34. Li CL, Sohn K, Yoon J et al (2021) Cutpaste: Self-supervised learning for anomaly detection and localization. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9664–9674

  35. Yi J, Yoon S (2020) Patch svdd: Patch-level svdd for anomaly detection and segmentation. In: Proceedings of the Asian conference on computer vision, pp 375–390

  36. Bergmann P, Fauser M, Sattlegger D et al (2020) Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4183–4192

  37. Wang G, Han S, Ding E, et al (2021) Student-teacher feature pyramid matching for anomaly detection. arXiv:2103.04257

  38. Bergmann P, Fauser M, Sattlegger D et al (2019) Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9592–9600

  39. Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334

    Article  MATH  Google Scholar 

  40. Zhang K, Zuo W, Zhang L (2018) Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. IEEE Trans Image Process 27(9):4608–4622

    Article  MathSciNet  MATH  Google Scholar 

  41. Roth S, Black MJ (2005) Fields of experts: A framework for learning image priors. In: 2005 IEEE Computer society conference on computer vision and pattern recognition (CVPR’05), IEEE, pp 860–867

  42. Deng J, Dong W, Socher R et al (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. Ieee, pp 248–255

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 6207072223). We would like to thank haifeng xia for providing the STPM network construction code.

Author information

Authors and Affiliations

Authors

Contributions

The authors confirm contribution to the paper as follows: Literature search and review: HS-S, YF-B; Data collection: HX-L, SY-Z; Analysis and interpretation of results: HX-L, HS-S; Manuscript Editing: HX-L, YF-B; Draft manuscript preparation: SY-Z, YF-B. All authors reviewed the results and approved the final version of the manuscript.

Corresponding author

Correspondence to Yongfeng Bu.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, H., Song, H., Zhang, S. et al. Highway spillage detection using an improved STPM anomaly detection network from a surveillance perspective. Appl Intell 55, 7 (2025). https://doi.org/10.1007/s10489-024-06066-w

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-06066-w

Keywords