Deep Learning for Semantic Segmentation of Coral Images in Underwater Photogrammetry

Abstract

Abstract. Regular monitoring activities are important for assessing the influence of unfavourable factors on corals and tracking subsequent recovery or decline. Deep learning-based underwater photogrammetry provides a comprehensive solution for automatic large-scale and precise monitoring. It can quickly acquire a large range of underwater coral reef images, and extract information from these coral images through advanced image processing technology and deep learning methods. This procedure has three major components: (a) Generation of 3D models, (b) understanding of relevant corals in the images, and (c) tracking of those models over time and spatial change analysis. This paper focusses on issue (b), it applies five state-of-the-art neural networks to the semantic segmentation of coral images, compares their performance, and proposes a new coral semantic segmentation method. Finally, in order to quantitatively evaluate the performance of neural networks for semantic segmentation in these experiments, this paper uses mean class-wise Intersection over Union (mIoU), the most commonly used accuracy measure in semantic segmentation, as the standard metric. Meanwhile, considering that the coral boundary is very irregular and the evaluation index of IoU is not accurate enough, a new segmentation evaluation index based on boundary quality, Boundary IoU, is also used to evaluate the segmentation effect. The proposed trained network can accurately distinguish living from dead corals, which could reflect the health of the corals in the area of interest. The classification results show that we achieve state-of-the-art performance compared to other methods tested on the dataset provided in this paper on underwater coral images.

Fine-Grained 3D Modeling and Semantic Mapping of Coral Reefs Using Photogrammetric Computer Vision and Machine Learning

“…The conventional approach of conducting manual local in situ underwater surveys has historically been the primary method for assessing coral distributions and growth health. However, this method requires a significant investment of time during field diver surveys, leading to limitations in the spatial and temporal scales at which ecological surveys can be conducted [ 8 ]. Over the past decade, advancements in mapping benthic habitats have been made through satellite and aerial photogrammetry and remote sensing techniques [ 9 , 10 , 11 ].…”

“…These methods offer a rapid means of acquiring information for large-scale coral-monitoring projects and enable the identification of various benthic function types on coral reefs. Nonetheless, due to the water surface effect and pixel mixing, they cannot provide detailed and accurate observations of the complex structures of coral reefs [ 8 , 12 ]. The emergence of underwater photogrammetry and unmanned underwater vehicles have significantly enhanced data collection capabilities for underwater surveys, facilitating the high-resolution monitoring of coral reef observations at millimeter-level precision and enabling the observation of individual corals [ 13 , 14 , 15 ].…”

Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation

“…Through AI-assisted image segmentation, the labor required to measure and identify ecologically relevant objects, such as the sizes and identities of coral colonies, can be automated, thus decreasing the time required for this task and increasing the amount of information that can potentially be acquired. Deep learning methods have been implemented to greatly increase the efficiency with which complex or irregular objects, such as coral colonies, can be segmented from images [28,29]. Additionally, they enable reefscale changes in rugosity and structure to be detected over time [30].…”