LSYOLO-Tracker: A vision algorithm for efficient Monopterus albus detection and tracking (original) (raw)
References
- Cheng, H., He, Y., Zhou, R.: Swamp eel (monopterus albus). Trends Genet. 37(12), 1137–1138 (2021)
Article Google Scholar - Liang, H., Guo, S., Li, Z., Luo, X., Zou, G.: Assessment of genetic diversity and population structure of swamp eel monopterus albus in china. Biochem. Syst. Ecol. 68, 81–87 (2016)
Article Google Scholar - Yuan, Q., Lv, W., Huang, W., Sun, X., Bai, N., Zhou, W.: Improving water quality following the death of asian swamp eel (monopterus albus) in cage culture through monitoring of ammonia nitrogen levels. Aquac. Res. 51(2), 696–706 (2020)
Article Google Scholar - Matsumoto, S., Kon, T., Yamaguchi, M., Takeshima, H., Yamazaki, Y., Mukai, T., Kuriiwa, K., Kohda, M., Nishida, M.: Cryptic diversification of the swamp eel monopterus albus in east and southeast asia, with special reference to the ryukyuan populations. Ichthyol. Res. 57, 71–77 (2010)
Article Google Scholar - Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
- Cai, K., Miao, X., Wang, W., Pang, H., Liu, Y., Song, J.: A modified yolov3 model for fish detection based on mobilenetv1 as backbone. Aquacult. Eng. 91, 102117 (2020)
Article Google Scholar - Yu, G., Luo, Y., Deng, R.: An detection algorithm for golden pomfret based on improved yolov5 network. SIViP 17(5), 1997–2004 (2023)
Article Google Scholar - Gao, Y., Li, Z., Zhang, K., Kong, L.: Gcp-yolo: a lightweight underwater object detection model based on yolov7. J. Real-Time Image Proc. 22(1), 1–13 (2025)
Article Google Scholar - Cai, C., Tan, S., Wang, X., Zhang, B., Fang, C., Li, G., Xu, L., Liu, S., Wang, R.: Real-time detection of hypoxic stress behavior in aquaculture fish using an enhanced yolov8 model. Aquacult. Int. 33(3), 207 (2025). https://doi.org/10.1007/s10499-025-01886-0
Article Google Scholar - Ariza-Sentís, M., Vélez, S., Martínez-Peña, R., Baja, H., Valente, J.: Object detection and tracking in precision farming: a systematic review. Comput. Electron. Agric. 219, 108757 (2024)
Article Google Scholar - Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649 (2017). https://doi.org/10.1109/ICIP.2017.8296962
- Li, J., Liu, C., Wang, L., Liu, Y., Li, R., Lu, X., Lu, J., Shen, J.: Multi-species identification and number counting of fish passing through fishway at hydropower stations with ligtranet. Eco. Inform. 82, 102704 (2024)
Article Google Scholar - Wang, Y., Wu, D., Guo, Z., Peng, S.: Underwater target tracking technology based on yolo v4 and deepsort. In: 2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA), pp. 238–241 (2023). IEEE
- Si, G., Zhou, F., Zhang, Z., Zhang, X.: Tracking multiple zebrafish larvae using yolov5 and deepsort. In: 2022 8th International Conference on Automation, Robotics and Applications (ICARA), pp. 228–232 (2022). IEEE
- Mei, Y., Sun, B., Li, D., Yu, H., Qin, H., Liu, H., Yan, N., Chen, Y.: Recent advances of target tracking applications in aquaculture with emphasis on fish. Comput. Electron. Agric. 201, 107335 (2022)
Article Google Scholar - Li, Z., Luo, S., Xiang, J., Chen, Y., Luo, Q.: Improved chinese giant salamander parental care behavior detection based on yolov8. Animals 14(14), 2089 (2024). https://doi.org/10.3390/ani14142089
Article Google Scholar - Jiang, P., Ergu, D., Liu, F., Cai, Y., Ma, B.: A review of yolo algorithm developments. Procedia Comput. Sci. 199, 1066–1073 (2022). https://doi.org/10.1016/j.procs.2022.01.135
Article Google Scholar - Yang, L., Noh, T.: Yolov8-uw: innovative real-time algorithm for underwater object detection. SIViP 19(7), 1–15 (2025)
Article Google Scholar - Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision - ECCV 2016, pp. 21–37. Springer, Cham (2016)
Chapter Google Scholar - Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031
Article Google Scholar - Siripattanadilok, W., Siriborvornratanakul, T.: Recognition of partially occluded soft-shell mud crabs using faster r-cnn and grad-cam. Aquacult. Int. 32(3), 2977–2997 (2024). https://doi.org/10.1007/s10499-023-01307-0
Article Google Scholar - Erciyas, A., Barışçı, N., Ünver, H.M., Polat, H.: Improving detection and classification of diabetic retinopathy using cuda and mask rcnn. SIViP 17(4), 1265–1273 (2023)
Article Google Scholar - Lau, K.W., Po, L.-M., Rehman, Y.A.U.: Large separable kernel attention: Rethinking the large kernel attention design in cnn. Expert Syst. Appl. 236, 121352 (2024). https://doi.org/10.1016/j.eswa.2023.121352
Article Google Scholar - Zhang, H., Zhang, S.: Shape-iou: More accurate metric considering bounding box shape and scale. ArXiv arXiv:2312.17663 (2023)
- Bewley, A., Ge, Z., Ott, L., Ramos, F.T., Upcroft, B.: Simple online and realtime tracking. 2016 IEEE International Conference on Image Processing (ICIP), 3464–3468 (2016)
- Guo, M., Du, Y.: Classification of thyroid ultrasound standard plane images using resnet-18 networks. In: 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID), pp. 324–328 (2019). https://doi.org/10.1109/ICASID.2019.8925267
- Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: Efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531–11539 (2020). https://doi.org/10.1109/CVPR42600.2020.01155