# YOLOv5-FF: Detecting Floating Objects on the Surface of Fresh Water Environments
## 4 Experiments
### Performance Comparison  
We conduct experiments to evaluate the detection accuracy and speed of YOLOv5-FF by comparing YOLOv5-FF with several important object detection solutions including the two lightest versions of YOLOv8 [32], i.e., YOLOv8n and YOLOv8s, YOLOv7 [33], YOLOv5 [26], YOLOX [34], YOLOv4 [35], YOLOv3 [36], SSD [37], Faster-RCNN [38] and YOLOv5-CB [39]. Faster-RCNN is a two-stage detection solution, while all the other \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{\
\begin{tabular}{c} **New Detection** \\ **Layer** \\ \end{tabular} } & \multirow{2}{*}{**AFECM**} & \multirow{2}{*}{**FEEM**} & \multicolumn{2}{c}{
\begin{tabular}{c} **Prior Boxes with** \\ **K-Medoids Algorithm** \\ \end{tabular} } & \multirow{2}{*}{**mAP(\%)0.5**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Score**} \\ \hline Model1 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 78.00 & 71.80 & 79.88 \\ Model2 & ✓ & \(\times\) & \(\times\) & \(\times\) & 78.30 & 76.90 & 80.61 \\ Model3 & ✓ & ✓ & \(\times\) & \(\times\) & 78.40 & 72.50 & 79.46 \\ Model4 & ✓ & ✓ & ✓ & \(\times\) & 78.80 & 74.90 & 80.24 \\ Model5 (YOLO-FF) & ✓ & ✓ & ✓ & ✓ & 80.80 & 79.60 & 82.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation results. Figure 9: Some floating objects in FODS-00-01. (**a–e**) include only white_trash floating objects or plant_mixture floating objects, while (**f**) includes both classes of the floating objects. solutions are single-stage detection solutions. To ensure the fairness of training processes, the training parameters of batch_size and the number of iterations are separately set to 16 and 300, while all the other training parameters are configured with their default values. We evaluate the accuracy of YOLOv5-FF by four metrics, namely, AP, mAP, recall, and F1 score, and record the values of the four metrics for each solution in Table 3. According to the table, YOLOv5-FF detects plant_mixture objects with the highest AP. Compared with YOLOv8n, YOLOv8s, YOLOv7, YOLOv3, YOLOv5, YOLOv4, YOLOv3, SSD, Faster-CNN and YOLOv5-CB, it improves the AP in detecting plant_mixture objects by 6.5%, 5.3%, 22.2%, 12.5%, 2.3%, 30.7%, 3.9%, 25.94%, 12.06% and 7.7%, respectively. Table 3 also indicates that YOLOv5-FF detects white_trash objects in a higher AP than detecting plant_mixture objects. In the scenario of detecting white_trash objects, although YOLOv5-FF shows a slight degradation in AP compared to YOLOv3, it achieves a significant improvement in AP over all the other solutions. Concretely, it improves the AP in detecting white_trash objects by 16.9%, 16.6%, 9.2%, 8.2%, 3.2%, 22.7%, 52.42%, 50.3% and 2.3% comparing with YOLOv8n, YOLOv8s, YOLOv7, YOLOv3, YOLOv5, YOLOv4, SSD, Faster-CNN and YOLOv5-CB, respectively. AP can only evaluate the accuracy of YOLOv5-FF in detecting a certain type of floating objects, while mAP has the ability to evaluate the accuracy of YOLOv5-FF in detecting all types of floating objects. According to the results in Table 3, YOLOv5-FF detects floating objects with the highest mAP in all the solutions. Compared to YOLOv8n, YOLOv8s, YOLOv7, YOLOX, YOLOv5, YOLOv4, YOLOv3, SSD, Faster-CNN and YOLOv5-CB, YOLOv5-FF improves the mAP in detecting floating objects by 11.7%, 11%, 15.7%, 10.37%, 2.8%, 26.7%, 1.8%, 39.18%, 31.18% and 5%, respectively. Recall and F1 score are also utilized to evaluate the accuracy of YOLOv5-FF. Except YOLOvX, all the other solutions obtain valid Recall and F1 score, and YOLOv5-FF obtains the highest Recall and F1 score among all these solutions. Compared to YOLOv8n, YOLOv8s, YOLOv7, YOLOv5, YOLOv4, YOLOv3, SSD, Faster-CNN and YOLOv5-CB, YOLOv5-FF improves the Recall and F1 score in detecting floating objects by 18% and 12.26%, 17.2% and 10.03%, 19.9% and 14.87%, 7.8% and 2.47%, 14.9% and 37.35%, 8.2% and 2.72%, 60.61% and 50.89%, 25.08% and 22.03%, and 9.5% and 4.04%, respectively. Figure 10 shows the detection results of different solutions on the same image in FODS-00-01.