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A lightweight YOLOv8 integrating FasterNet for real-time underwater object detection

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In this paper, we propose a underwater target detection method that optimizes YOLOv8s to make it more suitable for real-time and underwater environments. First, a lightweight FasterNet module replaces the original backbone of YOLOv8s to reduce the computation and improve the performance of the network. Second, we modify current bi-directional feature pyramid network into a fast one by reducing unnecessary feature layers and changing the fusion method. Finally, we propose a lightweight-C2f structure by replacing the last standard convolution, bottleneck module of C2f with a GSConv and a partial convolution, respectively, to obtain a lighter and faster block. Experiments on three underwater datasets, RUOD, UTDAC2020 and URPC2022 show that the proposed method has mAP\(_{50}\) of 86.8%, 84.3% and 84.7% for the three datasets, respectively, with a speed of 156 FPS on NVIDIA A30 GPUs, which meets the requirement of real-time detection. Compared to the YOLOv8s model, the model volume is reduced on average by 24%, and the mAP accuracy is enhanced on all three datasets.

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Data availability

The data that support the findings of this study are openly available in RUOD, UTDAC2020, and URPC2022, at,, and, respectively.


  1. Moniruzzaman, M., Islam, S.M.S., Bennamoun, M., Lavery, P.: Deep learning on underwater marine object detection: a survey. In: Advanced Concepts for Intelligent Vision Systems: 18th International Conference, ACIVS 2017, Antwerp, September 18–21, 2017, Proceedings 18, pp. 150–160. Springer International Publishing (2017)

  2. Yang, L., Liu, Y., Yu, H., Fang, X., Song, L., Li, D., Chen, Y.: Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: a review. Arch. Comput. Methods Eng. 28, 2785–2816 (2021)

    Article  MathSciNet  Google Scholar 

  3. Chen, G., Mao, Z., Wang, K., Shen, J.: HTDet: a hybrid transformer-based approach for underwater small object detection. Remote Sens. 15(4), 1076 (2023)

    Article  Google Scholar 

  4. Zhang, W., Zhuang, P., Sun, H.H., Li, G., Kwong, S., Li, C.: Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement. IEEE Trans. Image Process. 31, 3997–4010 (2022)

    Article  Google Scholar 

  5. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, p. 28 (2015)

  6. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: single shot multibox detector. In: Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37. Springer International Publishing (2016)

  7. Glenn.: ultralytics/YOLOv5:v5.0 (2020).

  8. Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475 (2023)

  9. Zeng, L., Sun, B., Zhu, D.: Underwater target detection based on Faster R-CNN and adversarial occlusion network. Eng. Appl. Artif. Intell. 100, 104190 (2021)

    Article  Google Scholar 

  10. Song, P., Li, P., Dai, L., Wang, T., Chen, Z.: Boosting R-CNN: reweighting R-CNN samples by RPN’s error for underwater object detection. Neurocomputing 530, 150–164 (2023)

    Article  Google Scholar 

  11. Yu, G., Cai, R., Su, J., Hou, M., Deng, R.: U-YOLOv7: a network for underwater organism detection. Eco. Inform. 75, 102108 (2023)

    Article  Google Scholar 

  12. Hua, X., Cui, X., Xu, X., Qiu, S., Liang, Y., Bao, X., Li, Z.: Underwater object detection algorithm based on feature enhancement and progressive dynamic aggregation strategy. Pattern Recogn. 139, 109511 (2023)

    Article  Google Scholar 

  13. Xu, X., Liu, Y., Lyu, L., Yan, P., Zhang, J.: MAD-YOLO: a quantitative detection algorithm for dense small-scale marine benthos. Eco. Inform. 75, 102022 (2023)

    Article  Google Scholar 

  14. Glenn.: ultralytics/ultralytics: v8.0.136 (2023).

  15. Fayaz, S., Parah, S.A., Qureshi, G.J., Lloret, J., Del Ser, J., Muhammad, K.: Intelligent underwater object detection and image restoration for autonomous underwater vehicles. IEEE Trans. Veh. Technol. (2023)

  16. Talaat, F.M., ZainEldin, H.: An improved fire detection approach based on YOLO-v8 for smart cities. Neural Comput. Appl. 35(28), 20939–20954 (2023)

    Article  Google Scholar 

  17. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

  18. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, vol. 2018, pp. 8759–8768 (2018)

  19. Tan, M., Pang, R., Le, Q. V.: Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790 (2020)

  20. Chen, J., Kao, S.H., He, H., Zhuo, W., Wen, S., Lee, C.H., Chan, S.H.G.: Run, don’t walk: chasing higher FLOPS for faster neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12021–12031 (2023)

  21. Tian, Z., Shen, C., Chen, H., He, T.: Fcos: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019)

  22. Hu, K., Lu, F., Lu, M., Deng, Z., Liu, Y.: A marine object detection algorithm based on SSD and feature enhancement. Complexity 2020, 1–14 (2020)

    Google Scholar 

  23. Chen, X., Yuan, M., Yang, Q., Yao, H., Wang, H.: Underwater-YCC: underwater target detection optimization algorithm based on YOLOv7. J. Mar. Sci. Eng. 11(5), 995 (2023)

    Article  Google Scholar 

  24. Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Adam, H.: Searching for mobilenetv3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314–1324 (2019)

  25. Ma, N., Zhang, X., Zheng, H. T., Sun, J.: Shufflenet v2: practical guidelines for efficient cnn architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 116–131 (2018)

  26. Wu, C., Sun, Y., Wang, T., Liu, Y.: Underwater trash detection algorithm based on improved YOLOv5s. J. Real-Time Image Proc. 19(5), 911–920 (2022)

    Article  Google Scholar 

  27. Cui, J., Liu, H., Zhong, H., Huang, C., Zhang, W.: Lightweight transformers make strong encoders for underwater object detection. SIViP 17(5), 1889–1896 (2023)

    Article  Google Scholar 

  28. Terven, J., Cordova-Esparza, D.: A comprehensive review of YOLO: from YOLOv1 to YOLOv8 and beyond (2023). arXiv:2304.00501

  29. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IoU loss: faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 12993–13000 (2020)

  30. Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Yang, J.: Generalized focal loss: earning qualified and distributed bounding boxes for dense object detection. Adv. Neural. Inf. Process. Syst. 33, 21002–21012 (2020)

    Google Scholar 

  31. Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus) (2016). arXiv:1606.08415

  32. Xiao, J., Zhao, T., Yao, Y., Yu, Q., Chen, Y.: Context augmentation and feature refinement network for tiny object detection (2021)

  33. Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., Ren, Q.: Slim-neck by GSConv: a better design paradigm of detector architectures for autonomous vehicles (2022). arXiv:2206.02424

  34. Fu, C., Liu, R., Fan, X., Chen, P., Fu, H., Yuan, W., Luo, Z.: Rethinking general underwater object detection: datasets, challenges, and solutions. Neurocomputing 517, 243–256 (2023)

    Article  Google Scholar 

  35. Flyai.: Underwater object detection dataset (2020). Accessed 4 Nov

  36. National underwater robot competition (2022).

  37. Zhou, J., He, Z., Lam, K.M., Wang, Y., Zhang, W., Guo, C., Li, C.: AMSP-UOD: when vortex convolution and stochastic perturbation meet underwater object detection (2023). arXiv:2308.11918

  38. Xu, F., Wang, H., Peng, J., Fu, X.: Scale-aware feature pyramid architecture for marine object detection. Neural Comput. Appl. 33, 3637–3653 (2021)

    Article  Google Scholar 

  39. Yu, H., Li, X., Feng, Y., Han, S.: Multiple attentional path aggregation network for marine object detection. Appl. Intell. 53(2), 2434–2451 (2023)

    Article  Google Scholar 

  40. Gao, J., Geng, X., Zhang, Y., Wang, R., Shao, K.: Augmented weighted bidirectional feature pyramid network for marine object detection. Expert Syst. Appl. 237, 121688 (2024)

    Article  Google Scholar 

  41. Dong, X., Bao, J., Chen, D., Zhang, W., Yu, N., Yuan, L., Guo, B.: Cswin transformer: a general vision transformer backbone with cross-shaped windows. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12124-12134 (2022)

  42. Chen, Y., Zhang, Z., Cao, Y., Wang, L., Lin, S., Hu, H.: Reppoints v2: verification meets regression for object detection. Adv. Neural. Inf. Process. Syst. 33, 5621–5631 (2020)

    Google Scholar 

  43. Shi, Y., Gao, Z., Li, S.: Real-time detection algorithm of marine organisms based on improved YOLOv4-Tiny. IEEE Access 10, 131361–131373 (2022)

    Article  Google Scholar 

  44. Zhang, J., Yongpan, W., Xianchong, X., Yong, L., Lyu, L., Wu, Q.: YoloXT: a object detection algorithm for marine benthos. Eco. Inform. 72, 101923 (2022)

    Article  Google Scholar 

  45. Lyu, L., Liu, Y., Xu, X., Yan, P., Zhang, J.: EFP-YOLO: a quantitative detection algorithm for marine benthic organisms. Ocean Coast. Manag. 243, 106770 (2023)

    Article  Google Scholar 

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Guo, A., Sun, K. & Zhang, Z. A lightweight YOLOv8 integrating FasterNet for real-time underwater object detection. J Real-Time Image Proc 21, 49 (2024).

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