Coral reef monitoring is a highly laborious 6-step process. The problem is further compounded by the non-ideal water conditions in Singapore. Singapore's shallow and murky waters with strong undercurrents and high shipping traffic hinder the use of commercial off-the-shelf reef monitoring robots in local waters.

An automated and economical end-to-end solution was designed to solve the problem. An autonomous underwater vehicle (AUV) collects image data and transfers it to the cloud. Artificial intelligence then colour corrects, classifies and segments coral species before displaying results on an intuitive graphical user interface (GUI).

i. Portable

Weighing about 12kg, the AUV is lighter than most commercially available reef monitoring robots.

ii. Easy and safe to deploy (video 1)

A foldable handle was designed for the safety of the operator. With additional height, the operator need not bend over the boat excessively, thereby reducing the risk of falling overboard. He can also deploy the AUV single-handedly, giving him one free hand to hold onto the boat.

iii. Maneuverable (video 2)

Equipped with five thrusters, the AUV has five degrees of freedom: surge, heave, roll, pitch and yaw. Coupled with finetuned controllers, the AUV can maintain its path despite strong undercurrents and remain stable for the collection of high quality image data.

iv. Easy to operate and track (image 1)

Remote control of the AUV using a radio switch and a GUI allows the operator to turn on the AUV and track its battery levels, mission status and location remotely.

v. Long battery life (video 1)

A dual battery system reduces the time-consuming task of constantly recharging the AUV. An internal battery powers the electronics within the main hull. External batteries, which can be easily replaced, power the high energy consumption thrusters.

i. Collect high quality images

Equipped with a 12MP downward-facing camera, the AUV collects high-resolution coral images to ensure data quality.

ii. Comprehensive reef scannning

A lawn-mowing algorithm allows the AUV to scan an area of 400m² at each reef site.

i. Colour Correction (image 2)

Underwater images often suffer from colour degradation due to light absorption and scattering in water. State-of-the-art UGAN algorithm was used to show the true colour of corals, with higher contrast, less blur and less colour distortion. This helps to improve human understanding of the underwater scene.

ii. Coral Species Prediction (image 3)

• Corals can be difficult to identify due to their varying shapes and colours, even within a species, resulting in the need for references to coral identification guides. The species prediction algorithm based on the DenseNet architecture provides a 96% confidence that the correct species is in the top 3 predictions, helping to narrow down the options. 

iii. Coral Segmentation (image 4)

Manual tracing of a coral's outline is required to determine its size. It is a painstaking process done for every image taken during a coral monitoring dive. The coral segmentation algorithm with the famous UNet architecture and EfficientNet-B6 encoder serves to automatically generate the coral outline with a 95% F1 score, saving time and effort. 

"With the AUV covering more area across different sites, more information can be retrieved within a shorter period. With lesser money spent on boat charter, project funds for charters can be relocated to other resources or to expand the project scope. The AUV can also help researchers to assess the underwater conditions of unexplored or deeper sites."

- Toh Tai Chong, Senior Lecturer, College of Alice & Peter Tan, Tropical Marine Science Institute

"Cool! I am very impressed already. This is what is needed on the public domain! A lot of scientists are still using the old-school methods.

The GUI can potentially help to save a lot of time, especially in measuring coral areas and preliminary identifying the corals, so we can spend more time on data analysis and writing reports.

It can also be used as a tool to train researchers and citizen scientists in coral reef monitoring."

- Sam Shu Qin, Research Assistant, Tropical Marine Science Institute