# EXOLOv3: An Efficient Real-Time Detection Model for Floating Object on River
## 4 Experiments
### Experiments and Analysis
#### 4.3.4 Comparison with Other Methods  
We compare our model with other object detectors including improved RefineDet model we proposed in [35]. YOLOv3 uses the same parameter settings as our model in this paper, and Fast R-CNN and Faster R-CNN use the default parameter settings. Mask-Refined R-CNN, CA-faster R-CNN and Improved RefineDet train on the dataset we build until the model converges. We compare EYOLOv3 and the above methods on the same test dataset. The detection accuracy, the recall rate, the mAP, and FPS of the different detectors are shown in Table 6.  
It can be seen from Table 5, Fast R-CNN model, as a two-stage object detector, using the selective search strategy and high-level feature map, has the lowest detection accuracy and slowest detection speed, and missed detection for small objects is obvious. The detection accuracy of the Faster R-CNN network is slightly higher than Fast R-CNN and YOLOv3,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Precision** & **Recall** & **mAP** & **FPS** \\ \hline Fast R-CNN [24] & 79.8\% & 78.5\% & 75.3\% & 4 \\ Faster R-CNN [26] & 88.6\% & 84.9\% & 81.2\% & 13 \\ YOLOv3 [31] & 83.2\% & 80.4\% & 78.6\% & **35** \\ EYOLOv3 & 87.4\% & **85.7\%** & **82.3\%** & **35** \\ Mask-Refined R-CNN [28] & 86.9\% & 85.1\% & 81.8\% & 16 \\ CA-faster R-CNN [27] & **89.5\%** & 83.6\% & 82.0\% & 20 \\ Improved RefineDet [35] & 88.3\% & 85.0\% & 81.5\% & 28 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with other models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Precision** & **Recall** & **mAP** & **FPS** & **Graphics Card Memory Consumption** \\ \hline YOLOv3 [31] & 83.2\% & 80.4\% & 78.6\% & 35 & **1218 M** \\ YOLOv5 & 85.8\% & 82.5\% & 80.5\% & **40** & 1925 M \\ EYOLOv3 & **87.4\%** & **85.7\%** & **82.3\%** & 35 & 1250 M \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with other YOLOs.  
and its detection speed is also faster than Fast R-CNN, but slower than YOLOv3. YOLOv3 is a single-stage detection model, so its detection speed is fast. However, YOLOv3 uses the traditional NMS algorithm, so there is a problem of missed detection of floating objects and no candidate box extraction process, so the detection accuracy is low. Our proposed model achieves the highest detection accuracy, which is 82.3%, and the detection speed reaches 35 FPS, which meets the real-time detection requirements of video streams. Both Mask-Refined R-CNN and CA-faster R-CNN are improved by adding auxiliary network on the basis of R-CNN, which improves the detection accuracy. However, due to its two-stage object detection network, the detection speed is slower than EYOLOv3. Compared with the Improved RefineDet we proposed in the previous work, EYOLOv3 uses the idea of skip connection for reference to fuse low-level features into multiple high-level features and uses soft-NMS algorithm to effectively reduce the missed detection rate of highly overlapping objects. Consequently, the final feature map generated for detection in EYOLOv3 contains rich detail information, which is conducive to small object detection. And the experiments show EYOLOv3 has higher detection accuracy and speed than Improved RefineDet. The experiments are shown in Figure 11.  
Figure 11: _Cont._