2.3.1. Real-Time Bottom Tracking

By finding the first echo that reaches the seabed, the purpose of bottom tracking aims to accurately obtain the sonar height from the seabed, then geocode the sonar image, and fulfill other applications. Given that the trained network model can effectively understand the characteristics of the input samples, bottom tracking can be accomplished via the 1D-CNN recognition of local backscatter strength sequences. By traversing the ping data sequence in the propagation direction, the trained 1D-CNN recognizes the local backscatter strength sequence in each search window, and the predicted score of each local sequence can be obtained, as shown in Figure 5. The window size should be the same as the sample size.

**Figure 5.** Prediction result for a one-side ping data obtained by using the trained network model with approximately 300 samples. (**a**) shows the one-side ping backscatter strength sequences, and (**b**) shows the corresponding prediction scores of each window.

The scores of the backscatter strength sequence in each search window are treated as the prediction results of the trained 1D-CNN. When the sound beam arrives at the seabed, a high score can be obtained by using the 1D-CNN prediction. Meanwhile, the scores of the data sequences in the other positions are far lower. Therefore, the maximum score position can be used to determine the bottom position of each ping.

Given the symmetry between the port and starboard data of each ping, a bottom tracking of the port and starboard data is carried out. The predicted port and starboard scores are then used to check the results and to achieve improved robustness to noise. The bottom tracking result of each ping is obtained on the basis of the port and starboard results. The complete bottom tracking procedures for each recorded ping is shown in Figure 6.

The bottom tracking accuracy of the survey line as obtained by 1D-CNN is calculated by using Equation (6) below.

$$acc = \frac{N\_1}{N\_0} \tag{6}$$

where *N*<sup>1</sup> is the number of successful-bottom-tracked pings and *N*<sup>0</sup> is the total number of pings.

**Figure 6.** Flowchart of the real-time bottom tracking of side scan data.

### 2.3.2. Improving Speed: Narrow Search Range

The processing speed of the bottom tracking algorithm for each ping plays a key role in guaranteeing the real-time performance of the proposed method. Given the stringent hardware computing power requirements of the deep learning algorithm, the data sequences should be recognized in a limited search range instead of the whole ping. In the current common hardware platform (AMD R5-2600X CPU and GTX-2070 GPU), the relationship between different search scopes and corresponding times was analyzed and shown in Figure 7. The computing speed and search range are linearly dependent on the same hardware platform, which indicates that narrowing the search range can effectively improve the computing speed.

According to the continuity of the seabed terrain variation, the bottom tracking position (sonar height) of the former ping can be used as the initial search position, and the search range can be determined by the seabed terrain variation or bottom tracking position rate. The relationship between the bottom tracking position variation rate and search range is shown in Table 1. By combining the initial search position provided by the previous ping and the bottom tracking position variation rate between the previous pings, the search range of the proposed method can be adaptively controlled, to guarantee an excellent real-time performance.

**Figure 7.** The relationship between consuming time and search range. The experiment was tested on the platform with AMD R5-2600X and GTX-2070.


**Table 1.** Auto-Adapted Search Ranges Depending on the Bottom Position Variation.

#### 2.3.3. Improving Accuracy: Sample Data Augmentation

The accuracy and abundance of samples are key in ensuring an accurate bottom sequence recognition. However, traditional sampling methods are time consuming and require manual intervention to ensure enough accuracy. During its application, the trained network could process other types of side scan sonar data in other seabed environments. The network needs to learn the features of the new data by continuously increasing the number of samples to improve the recognition accuracy. In this paper, a continuous increase of the samples was realized by using the learning ability of the network and few manual assistances, as shown in Figure 8.

**Figure 8.** Flowchart of sample set establishment and augmentation.

As the number of samples increases, the recognition accuracy and robustness of the network can be further improved, and an accurate bottom tracking can be guaranteed.
