FE-YOLO: An Efficient Deep Learning Model Based on Feature-Enhanced YOLOv7 for Microalgae Identification and Detection
Source abstract
Abstract
The identification and detection of microalgae are essential for the development and utilization of microalgae resources. Traditional methods for microalgae identification and detection have many limitations. Herein, a Feature-Enhanced YOLOv7 (FE-YOLO) model for microalgae cell identification and detection is proposed. Firstly, the feature extraction capability was enhanced by integrating the CAGS (Coordinate Attention Group Shuffle Convolution) attention module into the Neck section. Secondly, the SIoU (SCYLLA-IoU) algorithm was employed to replace the CIoU (Complete IoU) loss function in the original model, addressing the issues of unstable convergence. Finally, we captured and constructed a microalgae dataset containing 6300 images of seven species of microalgae, addressing the issue of a lack of microalgae cell datasets. Compared to the YOLOv7 model, the proposed method shows greatly improved average Precision, Recall, mAP@50, and mAP@95; our proposed algorithm achieved increases of 9.6%, 1.9%, 9.7%, and 6.9%, respectively. In addition, the average detection time of a single image was 0.0455 s, marking a 9.2% improvement.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Cell Image Analysis Techniques · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
China Waterborne Transport Research Institute, Beijing 100088, China
School of Railway Intelligent Engineering, Dalian Jiaotong University, Dalian 116028, China
Funding
National Key Research and Development Program of China · Young Elite Scientist Sponsorship Program by CAST · Science and Technology Innovation Project of the China Waterborne Transport Research Institute · Liaoning Provincial Natural Science Foundation · Fundamental Research Funds for the Provincial Universities of Liaoning
References (26)
Show source-provided references
1. Wondraczek, L., Gründler, A., Reupert, A., Wondraczek, K., Schmidt, M.A., Pohnert, G., and Nolte, S. (2019). Biomimetic light dilution using side-emitting optical fiber for enhancing the productivity of microalgae reactors. Sci. Rep., 9.10.1038/s41598-019-45955-w ↗
2. Biohybrid microalgae robots: Design, fabrication, materials, and applications10.1002/adma.202303714 ↗
3. Reducing the dimensionality of data with neural networks10.1126/science.1127647 ↗
4. Qian, P., Zhao, Z., Liu, H., Wang, Y., Peng, Y., Hu, S., Zhang, J., Deng, Y., and Zeng, Z. (2020, January 20–24). Multi-target deep learning for algal detection and classification. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.10.1109/EMBC44109.2020.9176204 ↗
5. Samantaray, A., Yang, B., Dietz, J.E., and Min, B.C. (2018). Algae detection using computer vision and deep learning. arXiv.
6. Development of microfluidic green algae cell counter based on deep learning
7. Deglint, J.L., Jin, C., and Wong, A. (2019). Investigating the automatic classification of algae using the spectral and morphological characteristics via deep residual learning. Image Analysis and Recognition: Proceedings of the 16th International Conference, ICIAR 2019, Waterloo, ON, Canada, 27–29 August 2019, Springer International Publishing. Proceedings, Part II 16.
8. Identification and detection of microplastic particles in marine environment by using improved faster R–CNN model10.1016/j.jenvman.2023.118802 ↗
9. Detection of microalgae objects based on the Improved YOLOv3 model10.1039/D1EM00159K ↗
10. ResNeXt convolution neural network topology-based deep learning model for identification and classification of Pediastrum10.1016/j.algal.2020.101932 ↗
11. Fully convolutional neural network for detection and counting of diatoms on coatings after short-term field exposure10.1021/acs.est.0c01982 ↗
12. M-YOLO v8s: Classification and Identification of Different Microalgae Species Based on the Improved YOLO v8s Model for Prevention of Harmful Algal Blooms10.1021/acsestwater.4c00853 ↗
13. Improved YOLOv7 object detection algorithm
14. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv.
15. Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18–23). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00716 ↗
16. Algae-based flexible localized oxygen control around Cells: An approach leading to more Biomimetic microphysiological systems10.1016/j.cej.2024.158040 ↗
17. Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., and Ren, Q. (2022). Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles. arXiv.
18. Convolutional neural network-based automatic classification for algal morphogenesis10.1508/cytologia.83.301 ↗
19. Tian, Q., Wang, Z., and Cui, X. (2024). Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism. arXiv.10.54254/2755-2721/88/20241740 ↗
20. A flexible sigmoid function of determinate growth10.1093/aob/mcg029 ↗
21. What is an ROC curve?10.1136/emermed-2017-206735 ↗
22. Faster r-cnn: Towards real-time object detection with region proposal networks
23. 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.
24. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. European Conference on Computer Vision, Springer International Publishing.10.1007/978-3-030-58452-8_13 ↗
25. Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Wong, C., Yifu, Z., and Montes, D. (2022). ultralytics/yolov5: v6.2-YOLOv5 Classification Models, Apple M1, Reproducibility, ClearML and Deci.AI Integrations, Zenodo.
26. Jocher, G. (2024, November 10). Yolov8. GitHub. Available online: https://github.com/ultralytics/ultralytics.