A YOLOW Algorithm of Water-Crossing Object Detection
Source abstract
Abstract
Accurately identifying and locating water-crossing objects is of utmost importance for environmental protection. However, traditional detection algorithms often exhibit poor anti-interference performance and low detection accuracy in complex environments. To address these issues, this paper proposes a YOLOW algorithm based on a one-stage object detection algorithm for the automatic identification of water objects. The proposed algorithm incorporates two new modules, namely the SPDCS module and the SPPAUG module, to improve the model’s performance. Specifically, the SPDCS module retains all information in the channel dimension, thereby enhancing the model’s detection accuracy and recognition ability for water-crossing objects. The SPPAUG module performs multiscale feature fusion, which further improves the model’s detection accuracy and recognition ability. Moreover, the C2f module is introduced from YOLOv8 to increase the detection speed. Experimental results on a water-floating object dataset demonstrate that the improved YOLOW model outperforms the standard YOLOv5s algorithm, especially in water-crossing object detection. This research has significant implications for environmental monitoring and protection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Water Quality Monitoring Technologies · Underwater Vehicles and Communication Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer and Information, Hohai University, Nanjing 211100, China
Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Jiangsu Province · China Postdoctoral Science Foundation
References (56)
Show source-provided references
1. VAR-tree model based spatio-temporal characterization and prediction of O3 concentration in China10.1016/j.ecoenv.2023.114960 ↗
2. Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM10.1016/j.energy.2022.126125 ↗
3. Cheng, Y., Xu, H., and Liu, Y. (2021, January 10–17). Robust small object detection on the water surface through fusion of camera and millimeter wave radar. Proceedings of the Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada.10.1109/ICCV48922.2021.01498 ↗
4. Behnamian, A., Banks, S., White, L., Brisco, B., Millard, K., Pasher, J., Chen, Z., Duffe, J., Bourgeau-Chavez, L., and Battaglia, M. (2017). Semi-automated surface water detection with synthetic aperture radar data: A wetland case study. Remote. Sens., 9.10.3390/rs9121209 ↗
5. Shakmak, B., and Al-Habaibeh, A. (2015, January 3–5). Detection of water leakage in buried pipes using infrared technology; A comparative study of using high and low resolution infrared cameras for evaluating distant remote detection. Proceedings of the 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), IEEE, Amman, Jordan.10.1109/AEECT.2015.7360563 ↗
6. Imagenet classification with deep convolutional neural networks10.1145/3065386 ↗
7. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
8. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
9. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
10. Girshick, R. (2015, January 7–13). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.169 ↗
11. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks10.1109/TPAMI.2016.2577031 ↗
12. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017, January 22–29). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.322 ↗
13. Cai, Z., and Vasconcelos, N. (2018, January 18–22). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00644 ↗
14. Cascade R-CNN: High quality object detection and instance segmentation10.1109/TPAMI.2019.2956516 ↗
15. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
16. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
17. Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
18. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv.
19. Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv.
20. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2021, January 19–25). Scaled-yolov4: Scaling cross stage partial network. Proceedings of the IEEE/cvf Conference on Computer Vision and Pattern Recognition, Virtual Conference.10.1109/CVPR46437.2021.01283 ↗
21. Wang, C.Y., Yeh, I.H., and Liao, H.Y.M. (2021). You only learn one representation: Unified network for multiple tasks. arXiv.
22. Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv.
23. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 18–22). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00721 ↗
24. (2023, January 10). YOLO v8. Available online: https://github.com/ultralytics/ultralytics.
25. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11–14). Ssd: Single shot multibox detector. Proceedings of the Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.10.1007/978-3-319-46448-0_2 ↗
26. Tan, M., Pang, R., and Le, Q.V. (2020, January 14–19). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01079 ↗
27. Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea.
28. Fcos: A simple and strong anchor-free object detector
29. Ghiasi, G., Lin, T.Y., and Le, Q.V. (2019, January 15–20). Nas-fpn: Learning scalable feature pyramid architecture for object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00720 ↗
30. Kirillov, A., Girshick, R., He, K., and Dollár, P. (2019, January 15–20). Panoptic feature pyramid networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00656 ↗
31. Hu, M., Li, Y., Fang, L., and Wang, S. (2021, January 19–25). A2-FPN: Attention aggregation based feature pyramid network for instance segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual Conference.10.1109/CVPR46437.2021.01509 ↗
32. Dai, X., Chen, Y., Xiao, B., Chen, D., Liu, M., Yuan, L., and Zhang, L. (2021, January 19–25). Dynamic head: Unifying object detection heads with attentions. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual Conference.10.1109/CVPR46437.2021.00729 ↗
33. Li, Y., Mao, H., Girshick, R., and He, K. (2022, January 23–27). Exploring plain vision transformer backbones for object detection. Proceedings of the Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel. Proceedings, Part IX.10.1007/978-3-031-20077-9_17 ↗
34. Qiao, S., Chen, L.C., and Yuille, A. (2021, January 19–25). Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual Conference.10.1109/CVPR46437.2021.01008 ↗
35. Zhou, X., Wang, D., and Krähenbühl, P. (2019). Objects as points. arXiv.
36. Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Tomizuka, M., Li, L., Yuan, Z., and Wang, C. (2021, January 19–25). Sparse r-cnn: End-to-end object detection with learnable proposals. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual Conference.10.1109/CVPR46437.2021.01422 ↗
37. Zhou, D., Fang, J., Song, X., Guan, C., Yin, J., Dai, Y., and Yang, R. (2019, January 16–19). Iou loss for 2d/3d object detection. Proceedings of the 2019 International Conference on 3D Vision (3DV), IEEE, Quebec City, QC, Canada.10.1109/3DV.2019.00019 ↗
38. Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15–20). 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, Long Beach, CA, USA.10.1109/CVPR.2019.00075 ↗
39. AP-loss for accurate one-stage object detection10.1109/TPAMI.2020.2991457 ↗
40. A ranking-based, balanced loss function unifying classification and localisation in object detection
41. Oksuz, K., Cam, B.C., Akbas, E., and Kalkan, S. (2021, January 11–17). Rank & sort loss for object detection and instance segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision, Virtual Conference.10.1109/ICCV48922.2021.00300 ↗
42. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7–12). Distance-IoU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI conference on artificial intelligence, New York, NY, USA.10.1609/aaai.v34i07.6999 ↗
43. Zhu, B., Wang, J., Jiang, Z., Zong, F., Liu, S., Li, Z., and Sun, J. (2020). Autoassign: Differentiable label assignment for dense object detection. arXiv.
44. Feng, C., Zhong, Y., Gao, Y., Scott, M.R., and Huang, W. (2021, January 11–17). Tood: Task-aligned one-stage object detection. Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE Computer Society, Virtual Conference.10.1109/ICCV48922.2021.00349 ↗
45. Ge, Z., Liu, S., Li, Z., Yoshie, O., and Sun, J. (2021, January 19–25). Ota: Optimal transport assignment for object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual Conference.10.1109/CVPR46437.2021.00037 ↗
46. Li, S., He, C., Li, R., and Zhang, L. (2022, January 19–24). A dual weighting label assignment scheme for object detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.10.1109/CVPR52688.2022.00917 ↗
47. Wang, J., Song, L., Li, Z., Sun, H., Sun, J., and Zheng, N. (2021, January 19–25). End-to-end object detection with fully convolutional network. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual Conference.10.1109/CVPR46437.2021.01559 ↗
48. Pyramid methods in image processing
49. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
50. Singh, B., and Davis, L.S. (2018, January 18–22). An analysis of scale invariance in object detection snip. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00377 ↗
51. Singh, B., Najibi, M., and Davis, L.S. (2018). Sniper: Efficient multi-scale training. Adv. Neural Inf. Process. Syst., 31, Available online: https://proceedings.neurips.cc/paper/2018/hash/166cee72e93a992007a89b39eb29628b-Abstract.html.
52. Sunkara, R., and Luo, T. (2022). No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects. arXiv.10.1007/978-3-031-26409-2_27 ↗
53. Hu, J., Shen, L., and Sun, G. (2018, January 18–22). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00745 ↗
54. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the European conference on computer vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
55. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30, Available online: https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html.
56. Cheng, Y., Zhu, J., Jiang, M., Fu, J., Pang, C., Wang, P., Sankaran, K., Onabola, O., Liu, Y., and Liu, D. (2021, January 11–17). Flow: A dataset and benchmark for floating waste detection in inland waters. Proceedings of the IEEE/CVF International Conference on Computer Vision, Virtual Conference.10.1109/ICCV48922.2021.01077 ↗