Underwater Photography Research Articles

Development of Attentive-Based Trans-UNet and Cycle Generative Adversarial Networks for Underwater Image Enhancement

Researchers across the globe have been investigating underwater photographs and a way to capture pictures with outstanding clarity for the past couple of decades. Also, improving the obtained photographs is an exhausting task. Usually, applied underwater image-capturing devices are unable to acquire high-resolution photos underwater, and their maintenance is extremely costly. The underwater images include multiple flaws because of biological processes like attenuation and scattering. These photographs experience color distortion, blurriness, and low contrast effects. Technologies that utilize deep learning have become more popular among several research studies and have gradually grown in impact on society. Several techniques demand sets of training photos, but gathering such expected sets can be challenging because of the complex nature of the underwater environment. Generating and restoring an image from water is a difficult task that has gained prominence in recent days. By lowering graininess, adjusting, and refining the photos using deep learning models, the major goal is to enhance underwater images. To accomplish this objective, an intelligent attention-based deep learning model is proposed. In the first stage, the unrefined images are gathered from typical data sources. Further, the collected underwater images are fed into the model of Attentive-based Trans-UNet-CycleGAN (ATUNet-CGAN), where the Transformer-based UNet model is integrated with the Cycle Generative Adversarial Networks (GANs). Also, the attention mechanism process is involved in Trans-UNet-CycleGAN for improving the superiority of submarine images. Finally, the performance of the model is validated using different metrics and correlated among baseline approaches. Therefore, the proposed methodology outperforms the exploitation of better enhancement of image quality.

Improving Underwater Photogrammetric 3D Reconstruction Processing of Shipwreck Sites

Abstract. Applying image processing algorithms to enhance the clarity of underwater images can significantly assist in the visual interpretation of subsea environments. Taking advantage of techniques to maximise the information extracted from the captured input data to produce the highest quality output ensures a higher utilisation of the valuable recorded content which may have been collected at the high cost of a laborious diver survey or an expensive ROV (Remotely Operated Vehicle) expedition. Adopting photogrammetric 3D reconstruction processing in underwater imaging has enabled the creation of high-fidelity digital 3D models which have been used to map subsea infrastructure for maintenance and repair, coral reefs for ecological monitoring, and in the primary context of this article, shipwrecks sites for maritime archaeology and accident investigation. Alongside exploring algorithms to reduce the characteristic haze of underwater photography, this article illustrates the advantages of optimising the processes utilised at distinct stages of the photogrammetric 3D reconstruction workflow to improve the photorealism of the digital 3D models.

An effectual underwater image enhancement framework using adaptive trans-resunet ++ with attention mechanism

ABSTRACT The intricacy of the underwater setting makes it difficult for optical lenses to capture clear underwater photos without haze and colour distortion. Some studies use domain adaptation and transfer learning to address this issue, they aim to reduce the latent mismatch between composition and real-world data, making the space of latent data difficult to read and impractical to control. The background light is a crucial component of the decaying paradigm that directly impacts how well images are enhanced. Thus, to improve the quality of the images over the underwater, new deep-learning techniques are being designed in this paper. Here, the Adaptive Trans-ResUnet++ with Attention Mechanism-based model performs the real-time underwater image enhancement process. In addition, a novel Random Enhanced Artificial Gorilla Troops Optimizer algorithm model is used for optimising the parameters over the given model to further enhance the given model’s performance. A diverse quantitative and qualitative validation is also carried out to learn the enhancement of underwater image quality. The enhanced underwater image may be also useful in the underwater object detection process. Thus, the enhanced images obtained from the developed model are compared with the existing techniques to confirm the efficacy of the suggested underwater image enhancement process.

What is hidden under our pontoons? Abundance and distribution of filter feeders (bivalves and tunicates) in the port area revealed. Artificial intelligence: an interesting analysis tool?

Urbanization is particularly prevalent along the coast, causing a considerable change in the ecology of the habitats found there. Ports, docks and all the structures linked to this anthropization modify the coastal environment by providing new niches, but also new constraints. Thus, ports are ecosystems in their own right, although they are rarely studied as such. In Europe’s largest marina (La Rochelle, France), among the multitude of organisms inhabiting it, four taxa seem particularly interesting to study: Mytilidae, Ostreïdae, Pectinidae and ascidiacea. Because these taxa, which belong to the bivalve and tunicate groups, are the stewards of the health of the port environment both as bio-indicators and as engineering species. The establishment of a systematic and regular census allows us to study the evolution of their populations and to determine what influences their distribution. To have as less impact as possible on the fauna studied, the census was carried out by underwater photography. The study shows that the populations are partly conditioned by the hydrodynamics of the environment and by the anthropic activity which is carried out there. Indeed, this study, which was carried out in a particular context (before and after the COVID-19 health crisis), shows the importance of anthropic pressure, particularly on the bivalve communities. A large amount of data is needed to understand what precisely governs bivalve and tunicate populations. Therefore, an innovative method, using artificial intelligence to automate the analyses, was tested in this study. This promising method should facilitate the census by reducing the analysis time.

Enhancing Underwater Images through Multi-Frequency Detail Optimization and Adaptive Color Correction

This paper presents a novel underwater image enhancement method addressing the challenges of low contrast, color distortion, and detail loss prevalent in underwater photography. Unlike existing methods that may introduce color bias or blur during enhancement, our approach leverages a two-pronged strategy. First, an Efficient Fusion Edge Detection (EFED) module preserves crucial edge information, ensuring detail clarity even in challenging turbidity and illumination conditions. Second, a Multi-scale Color Parallel Frequency-division Attention (MCPFA) module integrates multi-color space data with edge information. This module dynamically weights features based on their frequency domain positions, prioritizing high-frequency details and areas affected by light attenuation. Our method further incorporates a dual multi-color space structural loss function, optimizing the performance of the network across RGB, Lab, and HSV color spaces. This approach enhances structural alignment and minimizes color distortion, edge artifacts, and detail loss often observed in existing techniques. Comprehensive quantitative and qualitative evaluations using both full-reference and no-reference image quality metrics demonstrate that our proposed method effectively suppresses scattering noise, corrects color deviations, and significantly enhances image details. In terms of objective evaluation metrics, our method achieves the best performance in the test dataset of EUVP with a PSNR of 23.45, SSIM of 0.821, and UIQM of 3.211, indicating that it outperforms state-of-the-art methods in improving image quality.

ANALYSING THE POTENTIAL OF CORAL REEF ECOSYSTEMS FOLLOWING THE COVID-19 PANDEMIC IN BONDALEM, BULELENG, BALI

Coastal and marine areas have considerable biological resource potential, because they are supported by three main ecosystems, namely mangrove ecosystems, seagrass beds and coral reefs. Coral reefs are one of the coastal ecosystems that contribute greatly to coastal areas. But behind the great potential, it is also very vulnerable to damage, both caused by nature and human activities on land and in sea waters. As an effort to maintain the existence of these ecosystems, ecosystem monitoring is needed to find out how much potential is still available in the coastal area. The purpose of this study was to determine and provide information related to the potential and condition of coral reefs in Bondalem Village. This research used descriptive quantitative method with purposive sampling technique. The data taken is primary data that includes observations of coral reefs based on the form of growth (lifeform). Coral reef data collection was carried out using the UPT (Underwater Photo Transect, Underwater Photo Transect) method. The results obtained, coral reefs at the observation station have a total of 18 lifeforms. The percentage of coral cover obtained at a depth of 3 meters has a coral cover ranging from 35.34%, while at a depth of 10 meters the result of coral cover is 26.30% (medium category). The coral reef ecosystem uniformity index ranges from 0.96 - 0.97 (high). The diversity index of coral reef ecosystems in Bondalem Village ranged from 1.25 which is included in the relatively medium category (1<H'<3). The dominance index of coral reef ecosystem in Bondalem Village ranged from 0.064 - 0.066 (low).

Competency Model: Professional Development of Leisure Scuba Diving Instructors in China

Scuba diving specialization is a multidimensional index consisting of behavioral, cognitive, and affective domains. Based on the literature, the recreational scuba divers’ specialization was an interesting research topic to understand further the competency of leisure scuba diving instructors and prepare for their professional development. Leisure diving instructors need to carry out important duties such as teaching, underwater tour guides, safety protection, and equipment installation. The limitations of recreational scuba divers in China impact their specialization and competency as instructors. The study examined the qualities of an excellent leisure scuba diving instructor and the necessary professional enhancement they needed to become excellent leisure scuba diving instructors. Through qualitative methods, it revealed that leisure scuba diving instructors in China were 31 years old and older and possessed positive competencies in terms of their diving professional knowledge, skills, and attitude. However, they were average or unexceptional in their career opportunities, training, and mentorship and scored low in underwater photography, post-conference with students, and self-evaluations which were integrated into the professional development program as the output of the study.

Tectonic ledges at the Ladoga Lake bottom

For the past few years, the Institute of Limnology of the Russian Academy of Sciences has been conducting geological and geomorphological studies of the bottom of Lake Ladoga. Starting from 2018, underwater photo and video cameras developed at the Institute began to be used in these studies. The use of this new research tool led to the discovery of a number of new facts about the structure of the bottom of Lake Ladoga. In particular, at the bottom of the northern part of Lake Ladoga, three subvertical ledges were found, composed of bedrock, up to 70–100 meters high. According to a number of features, such as the bedrock composition of rocks, morphology and spatial correspondence to known fault zones, these ledges can be recognized as morphological expressions of tectonic objects – faults of presumably fault type. The absence of visible traces of glacial processing on the bedrock blocks suggests the Holocene age of the formation of these ledges and, accordingly, the existence of modern active tectonic movements in the area of Lake Ladoga.

Deep Learning for Multilabel Classification of Coral Reef Conditions in the Indo‐Pacific Using Underwater Photo Transect Method

ABSTRACTBecause coral reef ecosystems face threats from human activities and climate change, coral reef conservation programmes are implemented worldwide. Monitoring coral health provides references for guiding conservation activities. However, current labour‐intensive methods result in a backlog of unsorted images, highlighting the need for automated classification. Few studies have simultaneously utilized accurate labels along with updated algorithms and datasets. This study aimed to create a dataset representing common coral reef conditions and associated stressors in the Indo‐Pacific. Concurrently, it assessed existing classification algorithms and proposed a new multilabel method for automatically detecting coral reef conditions and extracting ecological information. A dataset containing over 20,000 high‐resolution coral images of different health conditions and stressors was constructed based on the field survey. Seven representative deep learning architectures were tested on this dataset, and their performance was quantitatively evaluated using the F1 metric and the match ratio. Based on this evaluation, a new method utilizing the ensemble learning approach was proposed. The proposed method accurately classified coral reef conditions as healthy, compromised, dead and rubble; it also identified corresponding stressors, including competition, disease, predation and physical issues. This method can help develop the coral image archive, guide conservation activities and provide references for decision‐making for reef managers and conservationists. The proposed ensemble learning approach outperforms others on the dataset, showing state‐of‐the‐art (SOTA) performance. Future research should improve its generalizability and accuracy to support global coral reef conservation efforts.

Underwater Robot Target Detection Algorithm Based on YOLOv8

Although the ocean is rich in energy and covers a vast portion of the planet, the present results of underwater target identification are not sufficient because of the complexity of the underwater environment. An enhanced technique based on YOLOv8 is proposed to solve the problems of low identification accuracy and low picture quality in the target detection of current underwater robots. Firstly, considering the issue of model parameters, only the convolution of the ninth layer is modified, and the deformable convolution is designed to be adaptive. Certain parts of the original convolution are replaced with DCN v3, in order to address the issue of the deformation of underwater photos with fewer parameters and more effectively capture the deformation and fine details of underwater objects. Second, the ability to recognize multi-scale targets is improved by employing SPPFCSPC, and the ability to express features is improved by combining high-level semantic features with low-level shallow features. Lastly, using WIoU loss v3 instead of the CIoU loss function improves the overall performance of the model. The enhanced algorithm mAP achieves 86.5%, an increase of 2.1% over the YOLOv8s model, according to the results of the testing of the underwater robot grasping. This meets the real-time detection needs of underwater robots and significantly enhances the performance of the object detection model.

Kondisi Terumbu Karang Di Perairan Leato Selatan Kecamatan Dumbo Raya Provinsi Gorontalo

Coral reefs are one of the tropical aquatic ecosystems which have a very important function both for the organisms that build this ecosystem and the surrounding ecosystems, namely seagrass ecosystems and mangrove ecosystems. Coral reef ecosystems are coastal area resources that are very vulnerable to damage, especially caused by human behavior/surrounding communities. The aim of the research is to determine the percentage of coral cover and coral reef mortality index in South Leato waters. This research was carried out from February to March 2024, located in South Leato Waters, Dumbo Raya District, Gorontalo Province. The research uses the Underwater Photo Transect (UPT) method, where the data is processed and analyzed with CPCe software. Based on the percentage of live coral cover from each station, namely Station I of 48.53% is in the medium category, Station II of 27.56% is in the medium category, Station III of 39.97% is in the medium category. With an average live coral cover of 38.69%, it is in the medium category, referring to the Decree of the Minister of Environment No. 4 of 2001 concerning Standard Criteria for Coral Reef Damage. The percentage level of coral reef damage calculated using the Mortality Index is station I at 0.46, station II at 0.66, station III at 0.67. The average mortality index for coral reefs in South Leato waters is 0.59, falling into the high mortality index category.

Percentage of Live Coral Cover in The Regional Water Conservation Area of the Sawo – Lahewa Water Tourism Park, North Nias District

Coral reefs are vital ecosystems facing serious threats due to climate change and human activities. This research aims to evaluate the condition of live coral cover in the Sawo-Lahewa Aquatic Tourism Park (TWP) Regional Marine Conservation Area (KKPD), North Nias Regency, as an important step in conservation and sustainable management efforts. Data was collected at 8 research stations using the Underwater Photo Transect (UPT) method. The research results show that the condition of coral reefs in this area is in the damaged to moderate category, with the percentage of live coral cover ranging between 12.33-45.37% and an average of 26.65%. The highest percentage of coral cover was found on Sarangbaung Island (45.37%) in the medium category, followed by Makora Island (42.13%), Gita Island (35.35%), and Lafau Island (31.07%). Meanwhile, locations with the lowest cover in the damaged category include Gosobaohi_01 Island (18.25%), Gosobaohi_02 Island (15.60%), Sifahandro Sea Waters (13.13%), and Bengkuang Bay Waters (12.33%). This research also looks at the abundance of coral juveniles, coral fish, and megabenthos to provide a broad picture of the health of the coral reef ecosystem. The results of this research can become a scientific basis for developing more effective management strategies. Apart from that, it is also a form of supporting conservation efforts, helping restore damaged ecosystems, and increasing the resilience of coral reefs to future environmental threats.Keywords: Coral Reefs, Regional Marine Conservation Area, North Nias

Dolphins, sharks, and barnacles: Use of photographs to examine intra- and inter-specific interactions in bottlenose dolphins in Mozambique.

Understanding interactions within and between species is crucial to ecological research. However, for cetaceans such interactions can be difficult to observe in the field. Photographs offer an opportunity to study intra- and inter-specific interactions, by capturing 'snapshots' of their occurrence over space and time. At-surface and underwater photographs of bottlenose dolphins (Tursiops aduncus) inhabiting Ponta do Ouro Partial Marine Reserve (PPMR), Mozambique, were used to examine evidence of interactions with other dolphins, predators and ectoparasites. Intra-specific scarring levels significantly differed by sex and age class, with males displaying more scarring than females. Similarly, adults had more scarring than juveniles or calves. Shark bites significantly differed in their distribution across dolphin body areas, with the dorsal side being more frequently wounded than the ventral side. The presence of barnacles was exclusive to fluke, dorsal and pectoral fins, and showed strong seasonal trends. Overall, this study demonstrates the value of photographs for examining marine ecological interactions. It provides the first insights regarding dolphin social behaviour, predation risk and health for this population. These in turn will support future research into the population dynamics and conservation of the PPMR dolphins, which is urgently required in the face of locally increasing anthropogenic pressures.

Assessing the resilience of coral reefs against macroalgae invasion in the Sempu Island Nature Reserve, Malang District, Indonesia

Abstract. Isdianto A, Yorarizka PD, Aliviyanti D, Sari SHJ, Yanuar AT, Asadi MA, Setyanto A, Bintoro G, Lelono TD, Atikawati D, Fathah AL, Putri BM, Supriyadi, Luthfi OM. 2024. Assessing the resilience of coral reefs against macroalgae invasion in the Sempu Island Nature Reserve, Malang District, Indonesia. Biodiversitas 25: 2877-2887. The living coral cover in the Sempu Strait is known to be stuck in the range of 37% due to average data from 2009-2016. Several coral diseases have been documented in this region, which can result in the transformation of coral reefs into algae-dominated communities. This study examines the dynamic interplay between coral reefs and macroalgae within the marine ecosystems of the Sempu Island Nature Reserve, Malang District, Indonesia. Conducted across five research stations, our analysis utilized the Underwater Photo Transect (UPT) method, combined with image analysis tools CPCe and Image-J, and statistical analysis through SPSS. The findings indicate a persistent competition between corals and macroalgae, exacerbated by sedimentation from port activities and river discharges, and significant nutrient loads from human settlements. The average living coral coverage varied significantly, with a range from 12% to 53% across the study period, while macroalgae coverage showed an increase, especially in turf algae. The correlation analysis revealed a 'quite strong' relationship (r=0.783 between coral and macroalgae coverage, suggesting that increases in coral cover could potentially facilitate macroalgae proliferation under current environmental conditions. Notably, the study highlighted the critical role of water quality parameters such as temperature, pH, and salinity, which were within optimal ranges yet facilitated competitive interactions. This research underscores the complexity of coral-macroalgae relationships and the pressing need for integrated management strategies focusing on water quality and habitat preservation to ensure the resilience of these critical marine ecosystems.

DBMKA-Net:Dual branch multi-perception kernel adaptation for underwater image enhancement

In recent years, due to the dependence on wavelength-based light absorption and scattering, underwater photographs captured by devices often exhibit characteristics such as blurriness, faded color tones, and low contrast. To address these challenges, convolutional neural networks (CNNs) with their robust feature-capturing capabilities and adaptable structures have been employed for underwater image enhancement. However, most CNN-based studies on underwater image enhancement have not taken into account color space kernel convolution adaptability, which can significantly enhance the model’s expressive capacity. Building upon current academic research on adjusting the color space size for each perceptual field, this paper introduces a Double-Branch Multi-Perception Kernel Adaptive (DBMKA) model. A DBMKA module is constructed through two perceptual branches that adapt the kernels: channel features and local image entropy. Additionally, considering the pronounced attenuation of the red channel in underwater images, a Dependency-Capturing Feature Jump Connection module (DCFJC) has been designed to capture the red channel’s dependence on the blue and green channels for compensation. Its skip mechanism effectively preserves color contextual information. To better utilize the extracted features for enhancing underwater images, a Cross-Level Attention Feature Fusion (CLAFF) module has been designed. With the Double-Branch Multi-Perception Kernel Adaptive model, Dependency-Capturing Skip Connection module, and Cross-Level Adaptive Feature Fusion module, this network can effectively enhance various types of underwater images. Qualitative and quantitative evaluations were conducted on the UIEB and EUVP datasets. In the color correction comparison experiments, our method demonstrated a more uniform red channel distribution across all gray levels, maintaining color consistency and naturalness. Regarding image information entropy (IIE) and average gradient (AG), the data confirmed our method’s superiority in preserving image details. Furthermore, our proposed method showed performance improvements exceeding 10% on other metrics like MSE and UCIQE, further validating its effectiveness and accuracy.

Hourly to weekly variations in halite precipitation from the hypersaline Dead Sea: The role of evaporation, water cooling and freshwater plume stability

ABSTRACTThick‐bedded halite sequences in the geological record are characterized by lateral variations, reflecting spatiotemporal variations in environmental forcing. Although temporal variations have been examined over seasonal scales or longer durations, there is a paucity of information regarding the dynamics of halite deposition at shorter durations of days and hours. Additionally, the influence of water discharges and surface salinity gradients remains poorly understood. Here, for the first time, in situ, hourly to weekly halite accumulation rates were measured on the Dead Sea floor (ca 20 m water depth), the only modern deep and hypersaline water body. Novel underwater photography methods were developed to directly observe halite precipitation at the lake floor, coeval with monitoring of the environmental drives, i.e. evaporation, meteorological and limnological conditions. Two fundamental environments were explored: a diluted plume environment arising from spring discharge; and a mixed hypersaline environment. The focus was on deposition during winter when the halite accumulation rate is highest, and the relationship between evaporation and halite accumulation is most straightforward since the water column is vertically uniform. Significant spatial variations in halite accumulation were found between the two environments, along with hourly variations in each environment. In the mixed hypersaline environment, over intra‐diurnal timescales, evaporation plays a more significant role in halite precipitation, whereas temperature has a greater impact on seasonal changes. In the diluted plume environment, the stability of the buoyant plume controls the rate of halite deposition; during windstorms, the plume is stirred, with higher surface water salinity and higher rate of halite accumulation. However, under calm winds, the plume is stable, with diluted surface water, and the rate of halite accumulation is a few times lower. Following these new findings, the implications for the interpretation of halite deposits are discussed.

An Improved YOLOv8n Used for Fish Detection in Natural Water Environments.

To improve detection efficiency and reduce cost consumption in fishery surveys, target detection methods based on computer vision have become a new method for fishery resource surveys. However, the specialty and complexity of underwater photography result in low detection accuracy, limiting its use in fishery resource surveys. To solve these problems, this study proposed an accurate method named BSSFISH-YOLOv8 for fish detection in natural underwater environments. First, replacing the original convolutional module with the SPD-Conv module allows the model to lose less fine-grained information. Next, the backbone network is supplemented with a dynamic sparse attention technique, BiFormer, which enhances the model's attention to crucial information in the input features while also optimizing detection efficiency. Finally, adding a 160 × 160 small target detection layer (STDL) improves sensitivity for smaller targets. The model scored 88.3% and 58.3% in the two indicators of mAP@50 and mAP@50:95, respectively, which is 2.0% and 3.3% higher than the YOLOv8n model. The results of this research can be applied to fishery resource surveys, reducing measurement costs, improving detection efficiency, and bringing environmental and economic benefits.

Beneath the lens: Exploring the impacts of underwater photographers in marine-based tourism

As scuba diving proliferates in popularity as a nature-based recreational activity, the associated benefits and impacts are correspondingly emerging. Previous studies showed that underwater photographers are potentially the more impactful group of scuba divers. To further explore the impacts and behaviour of underwater photographers, this study attempted to predict their impacts through three photography-related variables, including species of interest, photography habit, and photography commitment through a case study in Hong Kong. A scuba diver survey was performed in summer 2021 to assess the impacts of divers, a follow-up questionnaire survey was delivered, and a total of 106 samples were collected. Findings showed that underwater photographers caused more negative impacts than ordinary scuba divers. Among underwater photographers, individuals more interested in conspicuous and actively moving species have more frequent contact with marine organisms. In contrast, those more interested in cryptic and sedentary species were more likely to cause intentional contact. Underwater photographers with greater commitment also showed more frequent and more intentional connections. Implications were drawn regarding underwater photographers' behaviors, and relevant suggestions were recommended to address the potential concerns. Management implicationThis study highlights the significant ecological impacts of underwater photographers who cause higher contact rates with the coral community, which causes more severe damage. The research suggested various policies for the protected areas managers to mitigate the advert ecological impacts from the recreational divers as follows:●Train underwater photographers on proper techniques to reduce ecological impact.●Regulate the use of professional photography devices in marine environments.●Implement comprehensive pre-dive briefings to promote eco-friendly diving practices.●Use supervised dives to ensure adherence to environmental guidelines.By adopting the above measurements, the marine ecosystem can be protected, which can promote sustainable tourism practices.

Improving the Quality and Performance of Underwater Image Classification using the CLAHE-CNN Method

Research on underwater image analysis is critical because of challenges such as color distortion, low contrast, and noise in images. Various methods have been proposed to overcome this problem. To improve the quality and classification of underwater photos, this study aims to ensure improved performance using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Convolutional Neural Networks (CNN) on underwater image datasets. The dataset consists of 500 JPG image with RGB channel and dimensions of 512 × 512 collected from online sources. There are classifications of sharks, eels, dolphins, sea rays, and whales in the underwater imagery dataset. The experiment was conducted using Python programming language on a computer that had 24GB RAM, Intel® Core™ i7-10510U CPU and hardware properties of Intel® HD Graphics graphics card. The results of this study show how CLAHE improved the CNN classification of underwater imagery by 0.91% in training data, 0.45% in validation data, and 2.02% in test data.

Fish Species Detection Using Deep Learning

Abstract: This research explores the application of deep learning techniques for fish species detection in underwater environments. convolutional neural networks (CNNs) trained on extensive datasets, the study aims to enhance the accuracy and efficiency of species identification. The proposed model demon- strates promising results in differentiating diverse fish species, contributing to advancements in aquatic ecology monitoring and biodiversity conservation. The integration of deep learning in fish species detection holds potential for improving our understanding of underwater ecosystems and supporting sustainable fisheries management. The relative abundance of fish pieces in their habitats on a regular basis and keeping an eye on population fluctuations, this are a crucial task for marine scientists andconservationists diverse automatic computer based fish sample methods have been demonstrated in underwater photos and videos as alternatives to time consuming hand sampling there isn’t however a perfect method for automatically detecting fish and classifying the species this is mostly due to the difficulties in producing clear underwater images and videos which include environmental fluctuations in lightning fish camouflage Dynamicbackdrops murky water low resolution shape deformations of moving fish.