# RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8
## 4 Analysis and Discussion of Experimental Results  
### Ablation Experiments on the DUO Dataset  
To assess the impact of the enhancements suggested in this paper, a series of ablation experiments on the DUO dataset were analyzed using YOLOv8's three original models of s, m, and l as a baseline. The findings of these experiments are presented in Table 2.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Models** & **GFLOPs** & **P** & **R** & **mAP@ 0.5** & **mAP@ 0.5:0.95** \\ \hline YOLOv8s & 28.4 & 0.852 & 0.769 & 0.85 & 0.661 \\ YOLOv8m & 78.7 & 0.855 & 0.792 & 0.867 & 0.69 \\ YOLOv8l & 164.8 & 0.891 & 0.772 & 0.866 & 0.696 \\ YOLOv8s + Ref-Dilated & **23.9** & 0.872 & 0.78 & 0.864 & 0.68 \\ YOLOv8m + Ref-Dilated & 59.7 & 0.86 & 0.806 & 0.874 & 0.701 \\ YOLOv8l + Ref-Dilated & 117.5 & 0.867 & 0.801 & 0.877 & 0.706 \\ YOLOv8s + Ref-Dilated + & **23.9** & 0.861 & 0.785 & 0.866 & 0.68 \\ Involution & 59.7 & 0.858 & 0.807 & 0.876 & 0.702 \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & 117.5 & 0.859 & 0.818 & 0.882 & 0.713 \\ Involution & 26.53 & 0.861 & 0.78 & 0.868 & 0.69 \\ YOLOv8m + Ref-Dilated + & & & & & \\ YOLOv8m + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & & \\ YOLOv8l + Ref-Dilated + & & & & & \\ YOLOv8l + Ref-Dilated + &The results are analyzed: compared with the base model, after improving the C2f module by using the Ref-Dilated module, the model computation is significantly reduced, and the GFLOPS of the three base models are reduced by 18.83%, 24.14%, and 28.7%, respectively, and taking YOLOv8s as the benchmark, all the performance metrics, [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95, are improved by 1.65% and 2.87%, respectively, indicating that the Ref-Dilated module's multi-scale enhancement of the receptive field and feature extraction capability in the C2f module improves the detection efficiency and thus significantly reduces the computing cost. This lightweight and efficient core improvement meets our expectations for the general needs of underwater small object detection. With the introduction of Involution in the backbone network, YOLOv8l shows uniformly tiny improvements in R, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95 of 2.12%, 0.57%, and 0.99%, respectively, without a significant increase in the amount of computation.