An Assessment of YOLO Architectures for Oil Tank Detection from SPOT Imagery

Abstract

Since it can be used to manage and estimate oil reserves, the inventory of oil tanks is essential for both the economy and the military applications. Considering oil tanks contain valuable materials required for transportation and industrial production, they are a significant type of target. Oil tank detection techniques have several uses, including monitoring disasters, preventing oil leaks, designing cities, and assessing damage. Huge amount of satellite imagery has recently been available and it is used in both the military and civil applications. The new spaceborne sensors' higher resolution enables the detection of targeted objects. Therefore, remote sensing instruments provide ideal tools for oil tank detection task. Conventional approaches for oil tank detection from high resolution remote sensing imagery generally relies on geometric shape, structure, contract differences and color information of the boundary or hand-crafted features. However, these methods come along with vulnerabilities and hence it can be challenging to obtain accurate detection in the presence of a number of disturbance elements, particularly a wide range of colours, size variations, and the shadows that view angle and illumination create. Therefore, deep learning-based methods can provide a big advantage for solution of this task. In this regard, this study employs four YOLO models namely YOLOv5, YOLOX, YOLOv6 and YOLOv7 for oil tank detection from high-resolution optical imagery. Our results show that YOLOv7 and YOLOv5 architectures provide more accurate detections with mean average precision values of 68.11% and 69.69%, respectively. The experiments and visual inspections reveal efficiency, generalization and transferability of these models.

Keywords

• Oil Tank
• Detection
• Deep Learning
• YOLO

References

1. AirbusGeo. (2021). Airbus Oil Storage Detection. https://www.kaggle.com/datasets/airbusgeo/airb us-oil-storage-detection-dataset
2. Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
3. Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., & Sun, J. (2021). Repvgg: Making vgg-style convnets great again. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,
4. Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430.
5. Gevorgyan, Z. (2022). SIoU Loss: More Powerful Learning for Bounding Box Regression. arXiv preprint arXiv:2205.12740.
6. He, K., Zhang, X., Ren, S., & Sun, J. (2014, 2014//). Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. Computer Vision – ECCV 2014, Cham.
7. Jocher, G. (2022). ultralytics/yolov5: v6. 2-YOLOv5 Classification Models, Apple M1, Reproducibility, ClearML and Deci. ai integrations. Zenodo. org.
8. Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., & Nie, W. (2022). YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976.

9. Liu, Z., Zhao, D., Shi, Z., & Jiang, Z. (2019). Unsupervised Saliency Model with Color Markov Chain for Oil Tank Detection. Remote Sensing, 11(9), 1089. https://www.mdpi.com/2072-4292/11/9/1089
10. Nelson, J., & Solawetz, J. (2020). Responding to the Controversy about YOLOv5. https://blog.roboflow.com/yolov4-versus-yolov5/
11. Ok, A. O., & Başeski, E. (2015). Circular Oil Tank Detection From Panchromatic Satellite Images: A New Automated Approach. IEEE Geoscience and Remote Sensing Letters, 12(6), 1347-1351. https://doi.org/10.1109/LGRS.2015.2401600
12. Qi, W. (2022). Object detection in high resolution optical image based on deep learning technique. Natural Hazards Research. https://doi.org/10.1016/j.nhres.2022.10.002
13. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition,
14. Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition,
15. Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
16. Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition,
17. Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696.
18. Wang, C. Y., Liao, H. Y. M., Wu, Y. H., Chen, P. Y., Hsieh, J. W., & Yeh, I. H. (2020, 14-19 June 2020). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW),
19. Wang, Y., Zhang, Q., Zhang, Y., Meng, Y., & Guo, W. (2019). Oil Tank Detection from Remote Sensing Images based on Deep Convolutional Neural Network. Remote Sensing Technology and Application, 34(4), 727-735. https://doi.org/10.11873/j.issn.1004-0323.2019.4.0727
20. Wu, Q., Zhang, B., Xu, C., Zhang, H., & Wang, C. (2022). Dense Oil Tank Detection and Classification via YOLOX-TR Network in Large-Scale SAR Images. Remote Sensing, 14(14), 3246. https://www.mdpi.com/2072-4292/14/14/3246
21. Xu, S., Zhang, H., He, X., Cao, X., & Hu, J. (2022). Oil Tank Detection With Improved EfficientDet Model. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. https://doi.org/10.1109/LGRS.2022.3183350
22. Yu, B., Chen, F., Wang, Y., Wang, N., Yang, X., Ma, P., Zhou, C., & Zhang, Y. (2021). Res2-Unet+, a Practical Oil Tank Detection Network for Large-Scale High Spatial Resolution Images. Remote Sensing, 13(23), 4740. https://www.mdpi.com/2072-4292/13/23/4740
23. Zhang, H., Wang, Y., Dayoub, F., & Sunderhauf, N. (2021). Varifocalnet: An iou-aware dense object detector. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,
24. Zhang, L., & Liu, C. (2020). Oil Tank Extraction Based on Joint-Spatial Saliency Analysis for Multiple SAR Images. IEEE Geoscience and Remote Sensing Letters, 17(6), 998-1002. https://doi.org/10.1109/LGRS.2019.2937355
25. Zhang, L., Shi, Z., & Wu, J. (2015). A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), 4895-4909. https://doi.org/10.1109/JSTARS.2015.2467377
26. Zhang, L., Wang, S., Liu, C., & Wang, Y. (2019). Saliency-Driven Oil Tank Detection Based on Multidimensional Feature Vector Clustering for SAR Images. IEEE Geoscience and Remote Sensing Letters, 16(4), 653-657. https://doi.org/10.1109/LGRS.2018.2878106
27. Zhang, W., Zhang, H., Wang, C., & Wu, T. (2005, 29-29 July 2005). Automatic oil tank detection algorithm based on remote sensing image fusion. Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05.,
28. Zhu, C., Liu, B., Zhou, Y., Yu, Q., Liu, X., & Yu, W. (2012, 22-27 July 2012). Framework design and implementation for oil tank detection in optical satellite imagery. 2012 IEEE International Geoscience and Remote Sensing Symposium,