Ocean image data augmentation in the USV virtual training scene

Figures & data

Figure 1. Virtual unmanned vehicle interaction platform of AirSim (Shah, Dey, Lovett, & Kapoor, 2018)

Figure 2. The operation interface of ocean scene image data collection based on Unity3D

Figure 3. Image data collection of ocean scenes on sunny, dusk and cloudy days. The image includes a variety of ships with obvious light changes. These image data samples are difficult to obtain in real scenario

Table 1. Data collected in different scenes at different time

Scenes Time	Normal (Sunny)	Dusk	Cloud	Night
00:00–06:00	○	○	○	●
06:00–08:00	●	●	●	○
08:00–12:00	●	●	●	○
12:00–16:00	●	●	●	○
16:00–18:00	●	●	●	○
18:00–24:00	○	○	○	●

Table 2. The number of different types of ships collected datum under different weather conditions

	Normal (Sunny)	Dusk	Cloud	Night
Cargo ship	100	150	150	100
Passenger ship	100	150	150	100
Yacht	100	150	150	100
Cruise ship	100	150	150	100
Fishing boat	100	150	150	100
Warship	100	150	150	100
Others	100	150	150	100

Figure 4. Major components of the unmanned surface vessel

Table 3. The control commands of USV

Steering the rudder	Thrusting the propeller
$\left\{-\frac{\pi }{3},-\frac{\pi }{4},-\frac{\pi }{6},0,\frac{\pi }{6},\frac{\pi }{4},\frac{\pi }{3}\right\}$	{0,1}

Figure 5. Typical application scenarios in Unity3D ML-Agents toolkit

Table 4. The proportion of diverse weather conditions in PASCAL VOC dataset

Dataset	Normal	Dusk	Night	Cloud
PASCAL VOC 07 training data	158	9	13	18
PASCAL VOC 12 training data	386	22	8	100
PASCAL VOC 07 test data	128	10	3	36

Table 5. Compare the accuracy of boat class with/without 500 virtual ocean scenes data on Pascal 07 and Pascal 07 + 12 dataset. The virtual ocean scenes images are collected in Unity3D

Dataset	Image size: 416*416	Image size: 544*544
PASCAL VOC 07 + 12	67.7%	68.4%
PASCAL VOC 12 + 12 + 500	69%	71.5%
PASCAL VOC 07	58.9%	62%
PASCAL VOC 07 + 500	58.5%	62%

Figure 6. POMDP modeled USV obstacle avoidance overall pipeline

Figure 7. Comparison among the results using our VURLOA algorithm, random policy and CDRLOA algorithm (Cheng & Zhang, 2017) average sailing speed during the USV obstacle avoidance training

Figure 8. Comparison among the results using our VURLOA algorithm, random policy and CDRLOA algorithm (Cheng & Zhang, 2017)

Shah, S., Dey, D., Lovett, C., & Kapoor, A. (2018). AirSim: High-fidelity visual and physical simulation for autonomous vehicles. In Field and service robotics (pp. 621–635). Springer.

 Google Scholar

Cheng, Y., & Zhang, W. (2017). Concise deep reinforcement learning obstacle avoidance for underactuated unmanned marine vessels. Neurocomputing, 272, 63–73.

 Web of Science ®Google Scholar

Data availability statement

The data referred to in this paper is not publicly available at the current time.