# Underwater Object Detection Method Based on Improved Faster RCNN
## 4 Experiments
### Ablation Experiments and Analysis  
This section progressively demonstrates the effectiveness of each module through ablation experiments. Since our proposed improvement scheme has several modules to be combined, in order to determine whether the addition of modules is effective, this section verifies the rationality of the combination of methods through ablation experiments. The ablation experiments take the Faster RCNN (Res2 Net101) as the main structure, and then add the GIOU algorithm, the OHEM algorithm, the Soft-NMS algorithm, and multi-scale training to it one by one. Then, we train, test, and validate on the same underwater image dataset. The underwater environment images are divided into training data samples, validation data samples and test data samples, with a ratio of 7:2:1. The learning rate is initially set to 0.001. In order to better understand the performance of the model and make adjustments in a convenient and timely manner, pay attention to the performance changes of the model every three iterations. If the performance does not improve, adjust the learning rate to 90% of the original in the next training. Use the Adam [43; 44; 45] optimizer to optimize the model. After 500 epochs of iterative training and learning, the learning rate drops to 0.0001 and the parameters of the underwater object detection model converge. The ablation experiment results of the improved Faster RCNN underwater object detection model are shown in Table 3.  
Figure 5: Results of different methods for underwater object detection. (**a**–**e**) are respectively the detection results based on Fast RCNN, Faster RCNN, YOLOV3, SSD, the improved Faster RCNN. The black creatures are sea cucumbers, the blue five-pointed creatures are starfishes, the green round creatures are scallops, and the black slander creatures are seaweeds in the images. As can be known from Table 3, the Faster RCNN (Res2 Net101) improves the accuracy of [EMAIL_ADDRESS] by 0.2% by replacing the original IOU algorithm with the GIOU algorithm. In order to overcome the problem of the unbalanced positive and negative samples, the OHEM technology is introduced. Through the experiments, it can be known that this operation increases [EMAIL_ADDRESS] by 0.4%. In order to optimize the bounding box mechanism of the model, the standard NMS is replaced with Soft-NMS and the model performance is slightly improved. Finally, by integrating various improved schemes, the improved Faster RCNN underwater object detection algorithm model is obtained. In the experiments, [EMAIL_ADDRESS] reaches 71.7%, which is 3.3% higher than the original Faster RCNN model. Therefore, the method is effective in underwater object detection. The precision-recall curves are shown in Figure 6, and it can be known that the recall curve and the accuracy curve can quickly converge. The average accuracy of sea cucumber, sea urchin, scallop, starfish and aquatic plants are given in the curve of recall rate, and the average accuracy of all detection classes. It can be seen from in Figure 6 that sea urchins are in the recall curve. The area is the largest, so the accuracy of underwater target detection is also the highest, and then for the detection of aquatic plants, starfish, sea cucumbers and scallops the accuracy decreases in turn. In terms of detection speed, the improved method proposed in this paper is better than the original Faster RCNN in a single map. The film detection time is 0.005 s longer. After the 500 th epochs Recall, Precision, [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 curves are stable, which shows that the proposed methods are effective in underwater object detection. It can be known from Figure 7 that the proposed and improved method has higher accuracy in underwater object detection--the unimproved Faster RCNN cannot detect the starfish in the lower right corner of Figure 7, while the improved method in this paper can detect the starfish with different shapes in the lower right corner of Figure 7--and the improved model is more robust.