Underwater Vision Research Articles

Comparing Environmental DNA Metabarcoding and Underwater Visual Census to Monitor Korean Coastal Fish Community

Comparing Environmental DNA Metabarcoding and Underwater Visual Census to Monitor Korean Coastal Fish Community

Ecological indices from environmental DNA to contrast coastal reefs under different anthropogenic pressures

AbstractHuman activities can degrade the quality of coral reefs and cause a decline in fish species richness and functional diversity and an erosion of the ecosystem services provided. Environmental DNA metabarcoding (eDNA) has been proposed as an alternative to Underwater Visual Census (UVC) to offer more rapid assessment of marine biodiversity to meet management demands for ecosystem health indices. Taxonomic information derived from sequenced eDNA can be combined with functional traits and phylogenetic positions to generate a variety of ecological indices describing ecosystem functioning. Here, we inventoried reef fish assemblages of two contrasting coastal areas of Curaçao, (i) near the island's capital city and (ii) in a remote area under more limited anthropogenic pressure. We sampled eDNA by filtering large volumes of seawater (2 × 30 L) along 2 km boat transects, which we coupled with species ecological properties related to habitat use, trophic level, and body size to investigate the difference in fish taxonomic composition, functional and phylogenetic indices recovered from eDNA metabarcoding between these two distinct coastal areas. Despite no marked difference in species richness, we found a higher phylogenetic diversity in proximity to the city, but a higher functional diversity on the more isolated reef. Composition differences between coastal areas were associated with different frequencies of reef fish families. Because of a partial reference database, eDNA only partly matched those detected with UVC, but eDNA surveys nevertheless provided rapid and robust species occurrence responses to contrasting environments. eDNA metabarcoding coupled with functional and phylogenetic diversity assessment can serve the management of coastal habitats under increasing threat from global changes.

MoFiM: A morphable fish modeling method for underwater binocular vision system

AbstractFish morphology is an essential basis for fishery management, as it can reflect the growth status of fishes. Noncontact 3D reconstruction of underwater fish is a new way to obtain fish morphology. While it is difficult to reconstruct fish on account of the inadequate information caused by fish swimming and poor underwater imaging. This article introduces a morphable fish modeling method for the underwater binocular vision system. First, we define a fish representation based on selected landmarks. Then, we propose a chirality‐supervision incorporated hourglass network to estimate fish orientation and fish 2D landmarks simultaneously, and calculate fish 3D landmarks by triangulation. Next, we propose a fish modeling method which is based on 3D landmarks and introduce the optimization procedure of fish modeling. Finally, we obtain the complete 3D fish model corresponding to the input images. To train our network and build a parametric model, we constructed an underwater vision dataset and fish instance dataset respectively. We conducted experiments with grass carp as an example, and the experimental results show that our method can achieve effective fish modeling and is useful for noncontact measurement of underwater fish.

A novel underwater color correction method based on underwater imaging model and generative adversarial network

Underwater vision has played an increasingly important role in ocean research, ocean military, and underwater fishing. The raw underwater images suffer from color distortion and poor image quality, which make underwater vision more challengeable than open air vision. To address the mentioned problems, a novel model for underwater image color correction is proposed in this paper. Firstly, a synthetic underwater image generating network is presented to overcome the lack of effective underwater training data. Specifically, it combines a background color prior model to generate synthetic underwater images via in-air images data. By the benefit of the color prior and imaging algorithm, the generating model can be with fewer number of parameters and more effective. Meanwhile, a simple yet effective model is proposed to train on the in-air images and corresponding rendered synthetic underwater images for color correction. Different from other image restoration models with multiple substructure or complicated construction, the proposed method has only few convolution layers and combining with a dense-like and a res-like structure. Finally, the enhanced results demonstrate the superiority of the proposed method, which performs favorably against the existing state of the art methods in both effectiveness and model size aspects.

Assessment of coral health status using two-dimensional Coral Health Index (2D-CHI): A preliminary study in Pulau Perhentian Marine Park, Malaysia

Identifying the key threats that influence coral reef ecosystems, one needs accurate information about coral health status to provide information for enhancing coral reef management. In this study, the health status of the coral reefs in the Pulau Perhentian Marine Park (PPMP) is characterized by using two ecological components, benthic (BI) and fish (FI) indexes, which were combined to form a two-dimensional Coral Health Index (2D-CHI). The BI was determined based on data collected using the Coral Video Transect (CVT) method, while the BI using the data collected using Underwater Visual Census (UVC). The findings showed that significant difference in percentage cover of biotic (live hard coral [HC], algae [ALG], and other invertebrates [OT]) and abiotic (dead coral [DC] and sand, silt, or rock [SR]) components between the islands largely was contributed by ALG, DC, and SR rather than HC. In total, 110 species were identified, with fish biomass ranging from 11.18 g m−2 to 193.62 g m−2. The total biomass among the site is mainly contributed by fish from the Pomacentridae family, comprised of small fish such as damselfish and clownfish. Based on the BI and FI values, the 2D-CHI in PPMP can be classified from “fair” to “very degraded”. The 2D-CHI revealed that 55.33% of the reefs were in a degraded state, and only one station was in fair condition. There were four survey stations recognized as being of a very degraded condition. The findings presented here suggest that local pressure such as sedimentation, nutrient enrichment, and heavy reliance on coral reefs (particularly due to tourism) are the potential threats to the reefs in PPMP. Therefore, strategic reef management is required to minimize impacts in order to improve the health of the fragile ecosystem.

Underwater vision enhancement based on GAN with dehazing evaluation

Underwater vision enhancement based on GAN with dehazing evaluation

Comparison of an In Situ Imaging Device and Net-Based Method to Study Mesozooplankton Communities in an Oligotrophic System

In the past several years, the capabilities of optical tools and in situ imaging devices have greatly expanded and are now revolutionizing the field of plankton research. These tools have facilitated the discovery of new plankton and enhanced the understanding of populations of fragile and gelatinous zooplankton. Imaging devices are becoming more accessible and regularly deployed on oceanographic studies and monitoring efforts. However, despite the increasing use of these tools, there are few studies which offer direct comparisons between in situ imaging devices and traditional-net based methods, especially in open-ocean, oligotrophic systems where plankton are sparser and less intensively sampled. This study compares estimates of mesozooplankton abundance calculated by net-tows and an Underwater Vision Profiler 5 (UVP5HD-DEEP) imaging system. Net tows were conducted with a Multiple Opening and Closing Nets with Environmental Sensing System (MOCNESS) device equipped with 153µm mesh. In total, four tows, each sampling eight distinct depth bins, were conducted aboard two cruises in the Sargasso Sea. Along each cruise, in situ images were collected using an Underwater Vison Profiler 5 (UVP5HD-DEEP). Using these methods, we estimated abundance of different mesozooplankton groups (>0.5 mm). Using established biovolume-biomass conversions, we also estimated the dry mass of certain zooplankton taxa. Furthermore, we address two methods for calculating density and biomass concentration from UVP data. Estimates of mesozooplankton abundance and biomass concentration were generally higher from MOCNESS methods than the UVP estimates across all taxa. It was found that there is not a reliable relationship between UVP estimates and MOCNESS estimates when directly comparing similar depth bins. Nonetheless, when integrating density and biomass concentrations throughout the water column, estimates are not significantly different between the methodology. This study addresses several important considerations for using in situ imaging tools and how to reconcile findings with traditional net-based methods.

Coupling Imaging and Omics in Plankton Surveys: State-of-the-Art, Challenges, and Future Directions

A major challenge in characterizing plankton communities is the collection, identification and quantification of samples in a time-efficient way. The classical manual microscopy counts are gradually being replaced by high throughput imaging and nucleic acid sequencing. DNA sequencing allows deep taxonomic resolution (including cryptic species) as well as high detection power (detecting rare species), while RNA provides insights on function and potential activity. However, these methods are affected by database limitations, PCR bias, and copy number variability across taxa. Recent developments in high-throughput imaging applied in situ or on collected samples (high-throughput microscopy, Underwater Vision Profiler, FlowCam, ZooScan, etc) has enabled a rapid enumeration of morphologically-distinguished plankton populations, estimates of biovolume/biomass, and provides additional valuable phenotypic information. Although machine learning classifiers generate encouraging results to classify marine plankton images in a time efficient way, there is still a need for large training datasets of manually annotated images. Here we provide workflow examples that couple nucleic acid sequencing with high-throughput imaging for a more complete and robust analysis of microbial communities. We also describe the publicly available and collaborative web application EcoTaxa, which offers tools for the rapid validation of plankton by specialists with the help of automatic recognition algorithms. Finally, we describe how the field is moving with citizen science programs, unmanned autonomous platforms with in situ sensors, and sequencing and digitalization of historical plankton samples.

Underwater vision enhancement technologies: a comprehensive review, challenges, and recent trends

Underwater vision enhancement technologies: a comprehensive review, challenges, and recent trends

Adaptive underwater vision sistems

Over the past few years, the market for uninhabited underwater vehicles has expanded significantly. Since the Re-search Institute of Television has experience in building television systems for underwater vehicles, the television systems used in existing vehicles were analyzed. Based on the analysis of the television systems installed on underwater vehicles, the places were identified, the modification of which makes it possible to improve the characteristics of television systems. A method is proposed to increase the autonomy of uninhabited underwater vehicles by adapting the operating mode of the television system.

Coral reefs recovery post bleaching event in Central Tapanuli, North Sumatra

Temperatures on the west coast of Sumatra, South Java, Bali, and Nusa Tenggara in the Indian Ocean rose dramatically in 2016. The extreme increase in temperature greatly affects sea surface temperature and coral reef ecosystems. Central Tapanuli waters were also the area that could not escape this phenomenon. Coral bleaching has caused damage to coral reefs which is characterized by decreasing live coral’s cover. Observations on coral reef recovery have been carried out at 13 sites in Central Tapanuli waters. Observations were made using the UPT (Underwater Photo Transect) method to determine live coral’s cover. The coral fish density was determined using the UVC (Underwater Visual Census) method. From the observations, coral cover decreased by 57,68% in 2016. Observations were made in 2015, 2016, 2018, and 2021. Observations in 2018 have shown increasing live coral cover by approximately 5%. Meanwhile, in 2021 there was an increase in live coral cover by approximately 8.25% from 2018. Even though coral composition changes and mostly the number of coral decreases, after 5 years, the number of corals starts to increase. The reef health index likewise rises in tandem with these changes. Despite some chronic stress being present at some places, the water quality was favorable for coral recovery.

Analysis of coral reef diversity and its correlation to fish abundance in Biawak Island cluster areas

The importance of coral reefs for humans and marine biota must be conserved due to threat of climate change and anthropogenic pressures, one of which is in a cluster of small islands in Indramayu Regency, (comprising Biawak, Candikian and Gosong island). This study aims to assess the status of coral reefs in Indramayu (percent cover, diversity, uniformity, and dominancy of the coral reef ecosystem) as well as the potential correlation with fish abundance. Areas of interest were pre-determined using Lyzenga method, ground truth using the LIT (Line Intercept Transect), while genus-based fish abundance using UVC (Underwater Visual Census) method. This study revealed that the status of coral reefs is in moderate condition. The diversity index ranges from 1 to 3, and dominancy index is less than 0.50, indicating a stable reef community where no dominant genus is found in the community. The correlation between fish abundance and the live coral cover percentage showed a positive linear association. These results indicate that current coral reef ecosystems seem unaffected by anthropogenic pressures. It is expected that these results can be used by local authorities to conserve coral reef ecosystems and marine biota, and to maintain the sustainability of community livelihoods.

Experimental Research on Overwater and Underwater Visual Image Stitching and Fusion Technology of Offshore Operation and Maintenance of Unmanned Ship

The new way of offshore operation and maintenance based on unmanned ships has outstanding advantages. Aiming at the problem of lack of overall understanding of the complex environment above and under the water surface during the operation and maintenance of unmanned ships, a stitching and fusion technology of overwater and underwater visual images for unmanned ships is proposed. The software and hardware framework of the overwater and underwater visual image fusion system is constructed, the image processing methods in different environments are defined, and the accurate acquisition of obstacle information is realized. In the two experimental scenarios, the stitching accuracy of the obstacle model based on an extended neighborhood method can reach more than 85% within the obstacle distance of 8 m and more than 80% within the obstacle distance of 14 m. An image-driven Frustum–PointNets detection algorithm is proposed to obtain comprehensive obstacle avoidance information. In addition, the average accuracy of the three-dimensional detection of the algorithm is up to 91.40%. These results are significant and have a good reference value, as it demonstrates that the stitching and fusion method can effectively obtain the comprehensive information of overwater and underwater objects for unmanned ship.

Correcting estimations of copepod volume from two‐dimensional images

AbstractAccurate plankton biomass estimations are essential to study marine ecological processes and biogeochemical cycles. This is particularly true for copepods, which dominate mesozooplankton. Such estimations can efficiently be computed from organism volume estimated from images. However, imaging devices only provide two‐dimensional (2D) projections of three‐dimensional objects. The classical procedures to retrieve volumes, based on the equivalent spherical diameter (ESD) or the best‐fitting ellipse, are biased. Here, we present a method to correct these biases. First, a new method aims to measure body area and fit an ellipse. Then, the body of copepods is modeled as an ellipsoid whose 2D silhouette is mathematically derived. Samples of copepod bodies are simulated with realistic shapes/sizes and random orientations. Their total volume is estimated from their silhouettes using the two classical methods and a correction factor is computed, relative to the known, total, volume. On real data, individual orientations and volumes are unknown but the correction factor still holds for the total volume of a large number of organisms. The correction is around −20% for the ESD method and + 10% for the ellipse method. When applied to a database of 150,000 images of copepods captured by the Underwater Vision Profiler, the corrections decreased the gap between the two methods by a factor of 54. In addition, the same procedure is used to evaluate the consequence of the bias in the estimation of individual volumes on the slopes of normalized biovolume size spectra and show that they are, fortunately, not sensitive to the bias.

Constraining the Particle Size Distribution of Large Marine Particles in the Global Ocean With In Situ Optical Observations and Supervised Learning

AbstractThe abundance and size distribution of marine particles control a range of biogeochemical and ecological processes in the ocean, including carbon sequestration. These quantities are the result of complex physical‐biological interactions that are difficult to observe, and their spatial and temporal patterns remain uncertain. Here, we present a novel analysis of particle size distributions (PSDs) from a global compilation of in situ Underwater Vision Profiler 5 (UVP5) optical measurements. Using a machine learning algorithm, we extrapolate sparse UVP5 observations to the global ocean from well‐sampled oceanographic variables. We reconstruct global maps of PSD parameters (biovolume [BV] and slope) for particles at the base of the euphotic zone. These reconstructions reveal consistent global patterns, with high chlorophyll regions generally characterized by high particle BV and flatter PSD slope, that is, a high relative abundance of large versus small particles. The resulting negative correlations between particle BV and slope further suggests synergistic effects on size‐dependent processes such as sinking particle fluxes. Our approach and estimates provide a baseline for an improved understanding of particle cycles in the ocean, and pave the way to global, three‐dimensional reconstructions of PSD and sinking particle fluxes from the growing body of UVP5 observations.

Development of Diving and Snorkeling Ecotourism Based on Natural Resources Potential of on Miang Island Kutai Timur Regency

Miang Island has potential coral reef resources that can be used for marine ecotourism of diving and snorkeling, but there has not been any related study yet. This study aims to analyze the condition of coral reef resources, analyze the suitability and carrying capacity of the area for ecotourism and develop a strategy for developing diving and snorkeling ecotourism on Miang Island. The method being used for coral reef data collection by Underwater Photo Transect and reef fish using the Underwater Visual Census method. To develop the strategy of developing ecotourism is analyzed by using the Interpretative Structural Modeling (ISM) method. The results showed that the condition of coral reefs on Miang Island was in the moderate category with 40% live coral coverage. The area that can be used for snorkeling tours is 24.39 ha with a carrying capacity of 976 tourist visits/day. Meanwhile, the area that can be used for diving tourism is 13.40 ha with the ability to receive visitation of 536 tourist visits/day. Based on these conditions, the ecotourism development strategy is formulated that protects and utilizes coral reef ecosystems as sustainable tourism objects, facilitates access to development capital, makes a consistent policy, and coordinates between related agencies.

Adaptive Learning Attention Network for Underwater Image Enhancement

Underwater images suffer from color casts and low illumination due to the scattering and absorption of light as it propagates in water. These problems can interfere with underwater vision tasks, such as recognition and detection. We propose an adaptive learning attention network for underwater image enhancement based on supervised learning, named LANet, to solve these degradation issues. First, a multiscale fusion module is proposed to combine different spatial information. Second, we design a novel parallel attention module(PAM) to focus on the illuminated features and more significant color information coupled with the pixel and channel attention. Then, an adaptive learning module(ALM) can retain the shallow information and adaptively learn important feature information. Further, we utilize a multinomial loss function that is formed by mean absolute error and perceptual loss. Finally, we introduce an asynchronous training mode to promote the network&#x2019;s performance of multinomial loss function. Qualitative analysis and quantitative evaluations show the excellent performance of our method on different underwater datasets. The code is available at: <uri>https://github.com/LiuShiBen/LANet</uri>.

Recovery for underwater image degradation with multi-stage progressive enhancement.

Optical absorption and scattering result in quality degradation of underwater images, which hampers the performance of underwater vision tasks. In practice, a well-posed underwater image recovery requires a combination of scene specificity and adaptability. To this end, this paper breaks down the overall recovery process into in-situ enhancement and data-driven correction modules, and proposes a Multi-stage Underwater Image Enhancement (MUIE) method to cascade the modules. In the in-situ enhancement module, a channel compensation with scene-relevant supervision is designed to address different degrees of unbalanced attenuation, and then the duality-based computation inverts the result of running a enhancement on inverted intensities to recover the degraded textures. In response to different scenarios, a data-driven correction, encoding corrected color-constancy information under data supervision, is performed to correct the improper color appearance of in-situ enhanced results. Further, under the collaboration between scene and data information, the recovery of MUIE avoids ill-posed response and reduces the prior dependence of specific scenes, resulting in a robust performance in different underwater scenes. Recovery comparison results confirm that the recovery of MUIE shows the superiority of scene clarity, realistic color appearance and evaluation scores. With the recovery of MUIE, the Underwater Image Quality Measurement (UIQM) scores of recovery-challenging images in the UIEB dataset were improved from 1.59 to 3.92.

Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information and salient target contours are extracted by fusing Chan–Vese and local binary fitting models. Then, the original underwater images are used to supplement the missing contour information by using the local image fitting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours. Moreover, the proposed model achieves the best results in the quantitative comparison of MAE (mean absolute error), ER (error rate), and DR (detection rate) indicators and provides reliable prior knowledge for target tracking and visual information mining.

Preliminary Assessment of Invasive Lionfish Pterois miles Using Underwater Visual Census Method in the Northeastern Mediterranean

Abstract Underwater visual census (UVC) is a commonly used approach for assessing fish density and biomass. Iskenderun Bay, comprising the Samandağ coast in Turkey, is the most important introduction pathway of alien species in the northeastern Mediterranean. In this study, the density, abundance, distribution and interaction of invasive lionfish or devil firefish Pterois miles were assessed using the UVC method on the Samandağ coast. The total transect area monitored on the Samandağ coast was 4.500 m2. Species richness, Shannon’s diversity index and rate of biomass were 25, 1.81 and 73.5 m2, respectively. Reduced diversity was observed with increasing depths. The sandy seafloor was a limiting factor for lionfish distribution. There was a significant positive correlation between lionfish total density and both the Shannon diversity index (r = 0.99, P&lt;0.001) and Shannon evenness index (r = 0.99, P&lt;0.05). A high number of significant positive correlations (P&lt;0.05) were detected between the occurrence of lionfish and the occurrence of both the native species and other alien species. The first stock assessment study of lionfish by the UVC method in Turkish marine waters revealed that native species are under very high pressure from lionfish species, especially Chromis, Sparids and Wrasses, indicating negative effects of lionfish on regional native biodiversity.