# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 4 Experiments
### Ablation Study  
To assess the effectiveness of different modules, we conducted ablation experiments on the underwater UTDAC2020 dataset, and the results are shown in Table 5. We compared our proposed method with the baseline YOLOv8s. When we replaced the YOLOv8's backbone Darknet-53 with FasterNet-T0, the model parameters, FLOPS, and model size decreased by 22.52% (8.6 M vs. 11.1 M), 23.59% (21.7 B vs. 28.4 B), and 22.73% (17 MB vs. 22 MB), respectively. However, AP only decreased by 0.81% (49.69% vs. 50.50%),  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Backbone** & **Input Size** & **mAP** & **Parameters (M)** \\ \hline Two-Stage Detector: & & & & \\ Faster RCNN [46] & VGGNet & 1000 \(\times\) 600 & 73.2 & 134.7 \\ Faster RCNN [5] & ResNet-101 & 1000 \(\times\) 600 & 76.4 & 60.13 \\ MR-CNN [47] & VGG16 & 1000 \(\times\) 600 & 78.2 & \textasci{-} \\ R-FCN [48] & ResNet50 & 1000 \(\times\) 600 & 77.4 & 31.9 \\ CoupleNet [49] & ResNet101 & 1000 \(\times\) 600 & 82.7 & \textasci{-} \\ DSOD300 [50] & DS/64-192-48-1 & 300 \(\times\) 300 & 77.7 & 14.8 \\ Boosting R-CNN [6] & ResNet50 & 1000 \(\times\) 600 & 81.9 & 43.6 \\ Boosting R-CNN* [6] & ResNet50 & 1000 \(\times\) 600 & 83.0 & 45.9 \\ \hline One-Stage Detector: & & & & \\ SSD512 [51] & VGG16 & 512 \(\times\) 512 & 76.8 & \textasci{-} \\ STDN513 [52] & DenseNet169 & 513 \(\times\) 513 & 80.9 & \textasci{-} \\ RefineDet512 [53] & VGG16 & 512 \(\times\) 512 & 81.8 & \textasci{-} \\ DSSD513 [54] & ResNet101 & 513 \(\times\) 513 & 81.5 & \textasci{-} \\ RetinaNet [40] & ResNet50 & 1000 \(\times\) 600 & 77.3 & 36.2 \\ FERNet [55] & VGG16 + ResNet50 & 512 \(\times\) 512 & 81.0 & \textasci{-} \\ DES512 [56] & VGG16 & 512 \(\times\) 512 & 81.7 & \textasci{-} \\ DFR512 [57] & VGG16 & 512 \(\times\) 512 & 81.1 & \textasci{-} \\ EFIPNet512 [58] & VGG16 & 512 \(\times\) 512 & 81.8 & \textasci{-} \\ FFBNet512 [59] & VGG16 & 512 \(\times\) 512 & 82.1 & \textasci{-} \\ \hline Lightweight detectors: & & & & \\ SqueezeNet-SSD [60] & SqueezeNet & 300 \(\times\) 300 & 64.3 & 5.5 \\ MobileNet-SSD [60] & MobileNet & 300 \(\times\) 300 & 68.0 & 5.5 \\ Pelee [61] & PeleeNet & 300 \(\times\) 300 & 70.9 & 6.0 \\ Tiny-DSOD [60] & G/32-48-64-80 & 300 \(\times\) 300 & 72.1 & 1.0 \\ \hline YOLO detectors: & & & & \\ YOLOv8n & Darknet-53 & 640 \(\times\) 640 & 80.4 & 3.0 \\ YOLOv8s & Darknet-53 & 640 \(\times\) 640 & 83.9 & 11.1 \\ YOLOv8m & Darknet-53 & 640 \(\times\) 640 & 86.1 & 25.9 \\ Ours & FasterNet-T0 & 640 \(\times\) 640 & 84.4 & 8.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons with different object detectors on PASCAL VOC dataset (The symbol * signifies enhanced version of the model.).  
demonstrating the effectiveness of FasterNet in lightweighting YOLOv8 for underwater environments. After adding the prediction head for small objects, AP increased by 1.2% (50.89% vs. 49.69%). Through experiments, we found that the low-resolution feature map detection head had an insignificant effect on the UTDAC2020 dataset and reduced its channel number, so the model size underwent a slight decrease instead of an increase.  
Finally, based on our experience, increasing the input image resolution had a significant effect on small-object detection. By increasing the image input's longer side from 640 pixels to 1280 pixels, we achieved a 1.06% (53.18% vs. 52.12%) improvement in AP. This enhances the model's underwater detection capabilities and meets various speed and accuracy requirements in different scenarios.