# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 5 Discussion  
### The Impact of High-Resolution Feature Maps (Prediction Head for Small Objects)  
In order to validate the role of the prediction head for the small objects module in small-object detection, we visualized it using a small-object sample image from the UTDAC2020 dataset. As shown in Figure 6, the top image displays the detection results of YOLOv8s + FasterNet-T0, while the bottom image shows the detection results of the YOLOv8s + FasterNet-T0 + prediction head for small objects (Phead). By comparing these two images, we can observe the significant improvements when using the prediction head for the small objects module in small-object detection.  
To further demonstrate the effectiveness of the prediction head for small objects module, we conducted an analysis from the perspective of feature maps using Figure 7. YOLOv8s has three detection heads, and we visualized nine examples of feature maps for each detection head, as shown in the left three columns in Figure 7. Our proposed method introduces an additional high-resolution feature map detection head (prediction head for small objects), totaling four detection heads. Similarly, we visualized nine examples of feature maps for each detection head, as shown in the middle three columns in Figure 7. Additionally, based on empirical knowledge, using large-sized input images increases the overall scale of feature maps in the network, which is advantageous for small-object detection. According to our experimental results, this indeed brings about substantial improvements. We also performed a feature map visualization for our method with the image input's longer side set to 1280 pixels as a comparison, as shown in the right three columns in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Setting** & **AP** & **Echinus** & **Starfish** & **Holothurian** & **Scallop** & **Parameters** & **FLOPs** & **Model Size** \\   Baseline-YOLOv8s & 50.50 & 52.46 & 55.38 & 40.36 & 53.80 & 11.1 & 28.4 & 22 \\ +FasterNet-T0 & 49.69 & 52.04 & 54.33 & 39.34 & 53.05 & 8.6 & 21.7 & 17 \\ +FasterNet-T0, +Phead & 50.89 & 53.28 & 55.21 & 39.94 & 55.15 & 8.0 & 30.7 & 16 \\ +FasterNet-T0, +Phead, +CA & 51.40 & 53.11 & 56.54 & 41.12 & 54.82 & 8.0 & 30.8 & 16 \\ +FasterNet-T0, +Phead, +CA & 52.12 & **53.92** & 56.85 & 42.51 & 55.22 & 8.5 & 25.5 & 17 \\ +DCNv2 (640) & & & & & & & & \\ +FasterNet-T0, +Phead, +CA, +DCNv2 (1280) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study on UTDAC2020.  
Figure 6: The detection results for a small object sample image from the UTDAC2020 dataset using YOLOv8s + FasterNet-T0 (**top**) and YOLOv8s + FasterNet-T0 + Phead (**bottom**).  
Figure 7: _Cont._  
Through observation, the object outlines in the first row of feature maps in Figure 7 are proven to be clearer (highlighted by yellow-green bright spots), while those in the second row are somewhat blurry, and the outlines are completely blurred from the third row onwards. The first row of Figure 7 represents the high-resolution feature map detection head that we added, further highlighting the effectiveness of the prediction head for small objects module. This also suggests that high-resolution feature maps are more conducive to detecting small objects.