# 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
Therefore, the traditional CA, although it can encode the coordinate information efficiently, is more sensitive to the noise disturbances of the underwater environment, whereas the ELA module has a higher robustness to the noise in the underwater environment without an extra computational increase. As can be seen from the results of the ablation experiments, the improved method in this paper improves P by 2.46%, R by 1.43%, [EMAIL_ADDRESS] by 2.47%, and [EMAIL_ADDRESS]:0.95 by 4.39% when using YOLOv8s as a benchmark at the cost of only a 3.3 GFLOPs improvement. When YOLOv8m is used as the benchmark, R is improved by 7.12%, [EMAIL_ADDRESS] by 2.54%, [EMAIL_ADDRESS]:0.95 by 4.31%, and the computation is reduced by 16.4 GFLOPs, i.e., a reduction of about 9.95%. All the indexes of the improved algorithm are better than the base model of YOLOv8, and the overall improvement can be considered to be more effective. This shows that the method in this paper not only reduces the model arithmetic more substantially, but also improves the detection accuracy,  
As can be seen from Table 3, compared with other models, the improved model of this paper is in the leading position in almost all evaluation metrics. Compared to AWF-YOLO, which utilizes an adaptive weighted feature pyramid network to enhance underwater object detection, and CEH-YOLO, which improves the comprehensiveness of object recognition by designing a composite detection module, our improved algorithms with the Ref-Dilated convolutional block's expansion of the model sensory field and the HBFPN module's fusion of different levels of feature information are more effective, with an improvement of [EMAIL_ADDRESS] by 11.0% and 0.4%, respectively, and an improvement of [EMAIL_ADDRESS]:0.95 by 2.5% and 1.4%, respectively. Even though the [EMAIL_ADDRESS] of FishDet-YOLO is almost the same as the model in this paper, [EMAIL_ADDRESS]:0.95 contains metrics with high IoU thresholds (e.g., 0.75, 0.85, etc.). At these thresholds, the regression quality (i.e., localization accuracy) of the bounding box is more critical. The small object detection branch optimizes the localization accuracy of small objects, leading to a boost in AP at high IoU thresholds, and thus outperforming FishDet-YOLO by 3.2% in [EMAIL_ADDRESS]:0.95. In addition, in terms of frames per second (FPS, referring to the number of images processed per second), all three versions of this improved model (RHS-YOLOv8s, m, and l) are 22.4%, 19.7%, and 29.2% higher than the corresponding original model, respectively, among which RHS-YOLOv8s has the highest FPS, which is sufficient to meet the high real-time detection requirements. RHS-YOLOv8l has the highest detection accuracy with a slightly lower FPS, however, it has an advantage over most of the other models, which makes it a better choice for scenarios that require lower real-time performance while pursuing higher detection accuracy. Therefore, the model in this paper is more suitable for the complex environment in which the underwater objects are located.