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A fine-grained dataset for sewage outfalls objective detection in natural environments

A fine-grained dataset for sewage outfalls objective detection in natural environments
  • Woodward, J., Li, J., Rothwell, J. & Hurley, R. Acute Riverine Microplastic Contamination Due to Avoidable Releases of Untreated Wastewater. Nature Sustainability. 4, 793–802 (2021).

    Article 

    Google Scholar 

  • Tong, Y. et al. Decline in Chinese Lake Phosphorus Concentration Accompanied by Shift in Sources Since 2006. Nat. Geosci. 10, 507–511 (2017).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Shao, P. et al. Mixed-Valence Molybdenum Oxide as a Recyclable Sorbent for Silver Removal and Recovery From Wastewater. Nat. Commun. 14, 1365 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Xu, J. et al. Response of Water Quality to Land Use and Sewage Outfalls in Different Seasons. Sci. Total Environ. 696, 134014 (2019).

    Article 
    CAS 

    Google Scholar 

  • Mendonça, A., Losada, M. Á., Reis, M. T. & Neves, M. G. Risk Assessment in Submarine Outfall Projects: The Case of Portugal. J. Environ. Manage. 116, 186–195 (2013).

    Article 
    PubMed 

    Google Scholar 

  • Alkhalidi, M. A., Hasan, S. M. & Almarshed, B. F. Assessing Coastal Outfall Impact On Shallow Enclosed Bays Water Quality: Field and Statistical Analysis. Journal of Engineering Research. (2023).

  • Wang, Y. et al. Automatic Detection of Suspected Sewage Discharge From Coastal Outfalls Based On Sentinel-2 Imagery. Sci. Total Environ. 853, 158374 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Zhang, J., Zou, T. & Lai, Y. Novel Method for Industrial Sewage Outfall Detection: Water Pollution Monitoring Based On Web Crawler and Remote Sensing Interpretation Techniques. J. Clean. Prod. 312, 127640 (2021).

    Article 
    CAS 

    Google Scholar 

  • Wu, X., Sahoo, D. & Hoi, S. C. H. Recent Advances in Deep Learning for Object Detection. Neurocomputing. 396, 39–64 (2020).

    Article 

    Google Scholar 

  • Huang, Y. & Wu, C. Evaluation of Deep Learning Benchmarks in Retrieving Outfalls Into Rivers with Uas Images. Ieee T. Geosci. Remote. 61, 1–12 (2023).

    Google Scholar 

  • Cao, Z., Kooistra, L., Wang, W., Guo, L. & Valente, J. Real-Time Object Detection Based On Uav Remote Sensing: A Systematic Literature Review. Drones. 7, 620 (2023).

    Article 

    Google Scholar 

  • Xu, H. et al. Uav-Ods: A Real-Time Outfall Detection System Based On Uav Remote Sensing and Edge Computing.: IEEE, 2022:1-9.

  • Wang, A. et al. Yolov10: Real-Time End-to-End Object Detection. Ithaca: Cornell University Library, arXiv.org, 2024.

  • Gong, Y., Liu, G., Xue, Y., Li, R. & Meng, L. A Survey On Dataset Quality in Machine Learning. Inform. Software Tech. 162, 107268 (2023).

    Article 

    Google Scholar 

  • Jiang, P., Ergu, D., Liu, F., Cai, Y. & Ma, B. A Review of Yolo Algorithm Developments. Procedia Computer Science. 199, 1066–1073 (2022).

    Article 

    Google Scholar 

  • Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J. & Zisserman, A. The Pascal Visual Object Classes (Voc) Challenge. Int. J. Comput. Vision. 88, 303–338 (2010).

    Article 

    Google Scholar 

  • Lin, Q., Ye, G., Wang, J. & Liu, H. Roboflow: A Data-Centric Workflow Management System for Developing Ai-Enhanced Robots. 5th Conference on Robot Learning (CoRL 2021). London, UK, 2021.

  • Tian, Y., Deng, N., Xu, J. & Wen, Z. A Fine-Grained Dataset Named iSOOD for Sewage Outfalls Objective Detection in Natural Environments. Zenodo (2024).

  • Vayssade, J., Arquet, R., Troupe, W. & Bonneau, M. Cherrychèvre: A Fine-Grained Dataset for Goat Detection in Natural Environments. Scientific Data. 10, 689 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lin, T., Maire, M., Belongie, S., Bourdev, L. & Girshick, R. Microsoft Coco: Common Objects in Context, 2014.

  • Wang, Y. et al. Remote Sensing Image Super-Resolution and Object Detection: Benchmark and State of the Art. Expert Syst. Appl. 197, 116793 (2022).

    Article 

    Google Scholar 

  • Padilla, R., Netto, S. L. & Da Silva, E. A. B. A Survey On Performance Metrics for Object-Detection Algorithms. IEEE, 2020:237-242.

  • Chen, C. & Lyu, F. Unmanned-System-Based Solution for Coastal Submerged Outfall Detection. IEEE, 2021:1768-1771.

  • Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You Only Look Once: Unified, Real-Time Object Detection.: IEEE, 2016:779-788.

  • Sun, X., Wang, P., Wang, C., Liu, Y. & Fu, K. Pbnet: Part-Based Convolutional Neural Network for Complex Composite Object Detection in Remote Sensing Imagery. Isprs J. Photogramm. 173, 50–65 (2021).

    Article 

    Google Scholar 

  • Liu, Z., Gao, Y., Du, Q., Chen, M. & Lv, W. Yolo-Extract: Improved Yolov5 for Aircraft Object Detection in Remote Sensing Images. Ieee Access. 11, 1742–1751 (2023).

    Article 

    Google Scholar 

  • Li, Y., Wang, J. & Shi, B. Comparison of Two Target Detection Algorithms Based On Remote Sensing Images. International Conference on Computer Information Science and Artificial Intelligence. Kunming, China, 2021.

  • Li, W., Feng, X. S., Zha, K., Li, S. & Zhu, H. S. Summary of Target Detection Algorithms. Journal of Physics: Conference Series. 1757, 12003 (2021).

    Google Scholar 

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