# Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea

Kilian Bürgi, Charles Bouveyron, Diane Lingrand, Benoit Derijard, Frédéric Precioso, Cécile Sabourault

► To cite this version:

Kilian Bürgi, Charles Bouveyron, Diane Lingrand, Benoit Derijard, Frédéric Precioso, et al. To-
wards a fully automated underwater census for fish assemblages in the Mediterranean Sea. Ecological
Informatics, 2025, 85, pp.102959. 10.1016/j.ecoinf.2024.102959. hal-04896273v2

HAL Id: hal-04896273

https://hal.science/hal-04896273v2

Submitted on 25 Feb 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

# Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea

Kilian Bürgi<sup>a, b</sup>, Charles Bouveyron<sup>b</sup>, Diane Lingrand<sup>c</sup>, Benoit Derijard<sup>a</sup>, Frédéric Precioso<sup>c</sup>, Cécile Sabourault<sup>a, *</sup>

## ARTICLE INFO

**Keywords:**

Diver operated video

Automated UVC

Deep learning

Object detection

Marine biology

Marine protected areas

## ABSTRACT

Assessing underwater biodiversity is labour-intensive and costly, but is crucial for measuring the extent of the decline in local fish stock. In most cases, Underwater Visual Census (UVC) is the preferred method, however this can be costly in terms of human effort and is limited by meteorological and logistical factors. Advances in technology allows the utilisation of more autonomous video recording methods (i.e. Remote Operated Vehicles (ROV)) which addresses these limitations. This study used a transect-wise UVC coupled with diver operated videos (DOV). For the video analysis, a comprehensive fully automated pipeline was developed to extract frames from DOV and perform colour correction. This pipeline integrates a YOLO-based model to detect 20 Mediterranean fish species and validate the presence or absence of each species within individual transects. This study was conducted to evaluate the feasibility of using video-based methods for UVC with minimal human-input. The result of automated video analysis were in agreement with manual video counting, validating the autonomous and bias-free procedure for video assessment. In conclusion, utilising a minimal-human-input video method liberates the data acquisition from limiting factors (i.e. meteorological and logistical) and automation of this video analysis significantly reduces the labour and time required. For future fieldwork campaigns, the video data collection protocol needs to be modified to better resemble traditional UVC and enhance this acquisition method.

## 1. Introduction

The marine environment is facing multiple stressors that have a significant impact on ecosystems (Gissi et al., 2021). Artificialisation of shorelines (Carranza et al., 2019), overfishing (Demirel et al., 2020), masstourism (Mejjad et al., 2022) and climate change (Doney et al., 2012; Smale et al., 2019) are impacting fish communities all over the world and especially in high touristic areas such as the French Riviera. A high demand for fish meat as protein source has led and continues to lead to a decline in fish populations in recent decades, up to 99 % in very extreme sites (Myers et al., 1997; Vasilakopoulos et al., 2014). There is an increasing need to survey and monitor the current state of fragile marine areas in order to conserve them. Marine protected areas (MPAs) are created for this purpose and function as safe havens for fish populations to recover and proliferate. Inside an MPA, human activities such

as fishing, diving or anchoring are limited or prohibited to ensure the decompression of the ecosystem. However, the efficient management of MPAs is complicated and policy decision require a careful and holistic data collection.

This data collection involves both, abiotic factors (i.e. bathymetric and physical data) and biotic factors such as biodiversity. Whilst abiotic factors can be measured in real- time with appropriate instruments, measures of biotic factors are either invasive (i.e., experimental fishing, catch and release or other methods involving the landing of the fish) or less invasive but very costly and labour-intensive (i.e., underwater visual census (UVC) or video-based methods). The least invasive approach should be prioritised because the health of the ecosystem should not be compromised to evaluate it.

UVC has been standardised for decades (Kulbicki and Sarramégna, 1999) and has the advantages of being well established. It forms the best

<footnote><sup>a</sup> Université Côte d'Azur, CNRS, ECOSEAS, Nice, France</footnote>

<footnote><sup>b</sup> Université Côte d'Azur, Inria, CNRS, Laboratoire J.A.Dieudonné, Maasai team, Nice, France</footnote>

<footnote><sup>c</sup> Université Côte d'Azur, Inria, CNRS, I3S, Maasai team, Nice, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: Kilian.BURGI@univ-cotedazur.fr (K. Bürgi), Charles.BOUVEYRON@univ-cotedazur.fr (C. Bouveyron), Diane.LINGRAND@univ-cotedazur.fr (D. Lingrand), Benoit.DERIJARD@univ-cotedazur.fr (B. Derijard), Frederic.PRECIOSO@univ-cotedazur.fr (F. Precioso), Cecile.SABOURAULT@univ-cotedazur.fr (C. Sabourault).</footnote>
global coverage of biodiversity assessment (Caldwell et al., 2016) but it suffers from diver bias that can - in high biodiversity regions - lead to a over- or under-estimation of abundance and species richness of up to 25 % (Mcclanahan et al., 2007). In addition to this bias, UVC is high-cost, requires a lot of expert knowledge and is heavily dependent on weather and logistics due to the importance of diver safety. To coun- teract these factors, unmanned recording techniques have been devel- oped. Most current video-assisted studies use baited remote underwater videos (BRUVs), which are temporary stationary cameras with bait attached at the front to lure fish in front of the camera to evaluate the biodiversity or other ecologically relevant information (Mclean et al., 2005). The methodology of BRUVs is less-invasive and gives good insight in terms of biodiversity but poor choices of bait and location can lead to a bias towards bait-particular fish species.

To avoid these biases, video capture was used in conjunction with traditional UVC (Grorud-Colvert et al., 2021), which enabled the diver to review the transect for confirmation or to evaluate the complexity or coverage of the transect substrate. The video recordings follow the diver's point of view and are hypothesized to capture a similar biodi- versity to that observed by the diver. With this technique, numerous videos were recorded, and inspecting them was time intensive. In this era of digitalisation, artificial intelligence and its subdisciplines are a vastly rising field of research in marine ecology (Malde et al., 2020; Rubbens et al., 2023) and can give greater insights on the images and videos recorded in many different contexts (Vabø et al., 2021; C. M. Wu et al., 2022). Deep learning (DL) has shown promising results in the analysis of underwater imagery (i.e. Jalal et al., 2020; Spampinato et al., 2016). For example, excellent and truly inspiring results have been achieved, with mean average precision (mAP) of 0.845 (Allken et al., 2021a) using a real and synthetically extended dataset of in-trawl im- ages (Allken et al., 2021b). More similar to the present study is for example Xu and Matzner, 2018. They achieved an mAP of 0.54 on different real life applicable datasets, and Knausgård et al., 2022 ach- ieved an mAP of 0.84 on a temperate fish dataset, in a study which helped to benchmark and improve this work. However, these studies evaluate videos derived from stationary cameras and focus on machine learning metrics (F1 score, recall, precision and mAP), leaving out bio- logical measures. Whilst the importance of the machine learning per- formance metrics is undeniable, in real-life applications the biological or methodological metrics need to be incorporated to ensure a bigger picture of the study is captured and not only focus on one aspect of it.

The present study focussed on a video-assisted UVC protocol and the use of DL to perform a presence/absence analysis of video transects more rapidly than a human can. The framework of You Only Look Once (YOLO) is widely used in marine computer vision (Mohamed et al., 2020; Muksit et al., 2022; Park and Kang, 2020; Priyankan and Fer- nando, 2021) and the version 7 (Wang et al., 2023) was used in this study as the DL model for fish detection after careful benchmarking with other architectures. In the first experiment, the model was evaluated in terms of its capability to detect 20 classes (the 19 most abundant local fish species and one 'Other' class). In a second experiment, the model was used to detect the same 20 species on a different dataset. Then the detections were transect-wise concatenated and formatted into a pres- ence/absence table for each of the species. This table was compared with manual video counts determined by a marine biology expert and with professional diver-gathered UVC data. This study thus seeks to compare the different perceptions of fish species diversity in the Mediterranean Sea that result from the three data collection methods, manual video counting, the artificial intelligence algorithm, and traditional UVC by scuba divers. The aim is to determine the degree of differences in the observed biodiversity, and to propose modifications to improve future fieldwork campaigns.

## 2. Material and methods

This section will provide a detailed discussion of the study area, data

collection specifications, preprocessing methods, benchmark for model selection, training of the final model, and evaluation metrics used in this study.

### 2.1. Study area and data collection

The training dataset ($DATA_T$) was gathered in eight different loca- tions in the Mediterranean Sea along the French Riviera, following the same UVC protocol on each site (Harmelin-Vivien et al., 1985). The depth ranged from 1 to 37 m and was carried out during the whole year in 2022 (cold and warm season) to cover the full range of conditions and possibilities of fish occurrences.

The experimental dataset ($DATA_E$) was recorded in October 2023 in and around two protected areas, one no-take zone (Cap Roux) and one Natura2000 site (Corniche Varoise), which both have elevated biodi- versity. The specific coordinates and meta data can be found in the supplementary material (Table S1). A total of 64 videos, each corre- sponding to a transect, from 14 sites (8 on seagrass meadows and 6 on rocky substrates) were evaluated and compared. Each site consists of 3 to 6 transects, depending on the availability of video recordings and UVC data from the divers.

The videos were obtained with GoPro HERO 9 cameras, mounted on the clipboards (Fig. 1) used by the divers to note the number of fish per species with their respective size category (variable number of cate- gories per species). The videos were recorded with a framerate of 24 frames per second (FPS) and full high definition resolution (1920x1080px). Frames were extracted from these recordings with a framerate of 1 FPS for $DATA_T$ and 5 FPS for $DATA_E$. Fish visible for less than 1 s (less than 5 frames) in the videos of $DATA_E$ will not be considered in the methodology evaluation as they were unlikely to be actual detections.

### 2.2. Image preprocessing

The frames were processed in a next step by a marine biology expert to guarantee correctly identified species in the videos. To ensure a good species coverage, 19 different species and an 'Other' class were labelled manually in the frames resulting in 13,033 images (131 videos in the training set and 47 independent videos in the test set) in $DATA_T$ with a total of 68,573 (train = 40,379, test = 28,194) individual fish labels (species breakdown in Table S3) and 8739 miscellaneous labels such as background and diver. The 'Other' class includes species (Table S2) that have insufficient occurrences in the test videos (n (100). Since there was a wide range of conditions in the videos, a preprocessing was applied to both datasets to enhance each image colour range. For this purpose, a pretrained UIEC²-Net model (Y. Wang et al., 2021) was utilised to enhance the images. As the last preprocessing step, images were rescaled to 960x960px.

### 2.3. Algorithm selection

To be sure to pick the most appropriate DL algorithm for the sub- sequent steps of the study, five different architectures were chosen and trained on the $DATA_T$ (Table 1). The Detectron2 platform (Y. Wu et al., 2019) provides well-established pre-trained DL architectures. The ar- chitectures chosen are, 1) Fast R-CNN (Girshick, 2015) with a RestNet50 backbone, Feature Pyramid Network (FPN) and Region Proposal Network (RPN) configuration; 2) Faster R-CNN (Ren et al., 2016) with a RestNeXt101 backbone and FPN; 3) RetinaNet (Lin et al., 2017) with a ResNet101 backbone, FPN and a specialised focal loss counteracting class imbalance; 4) YOLOv7 (Wang et al., 2023); and 5) YOLOv10 (A. Wang et al., 2024). All of these models were pre-trained on the Common Objects in Context (COCO v2017) dataset (Lin et al., 2014). The training on $DATA_T$ was run on a GPU cluster (NVIDIA A40 / A100 PCIe GPU) with a mechanism to save the best performing weights on the validation set to avoid overfitting.
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Example image of the GoPro 9 camera setup to record the diver's point of view along a transect. The background shows the measuring tape that kept the diver on track with the UVC protocol.</figcaption></figure>

**Table 1**

The benchmark to find the best performing architecture for this study at 50 % confidence threshold. Metrics recall, precision, f1 score and inference speed are presented. The best performing architectures are highlighted in bold for each metric.

<table><thead><tr><th>Architecture</th><th>Recall</th><th>Precision</th><th>F1 Score</th><th>Speed (im/s)</th></tr></thead><tbody><tr><td>Fast R-CNN + ResNet50 + RPN + FPN</td><td>0.50</td><td>0.62</td><td>0.56</td><td>6.69</td></tr><tr><td>Faster R-CNN + ResNeXt101 + FPN</td><td>0.51</td><td><strong>0.67</strong></td><td>0.61</td><td>2.83</td></tr><tr><td>RetinaNet + ResNet101 + FPN</td><td>0.23</td><td>0.62</td><td>0.34</td><td>5.13</td></tr><tr><td>YOLOv7</td><td><strong>0.62</strong></td><td>0.64</td><td><strong>0.63</strong></td><td>28.77</td></tr><tr><td>YOLOv10</td><td>0.28</td><td><strong>0.85</strong></td><td>0.52</td><td><strong>65.79</strong></td></tr></tbody></table>

**2.4. Final model training**

As a result of the benchmark (Table 1), YOLOv7 was used for the final model due to its speed and balanced recall and precision values. The model was then pre-trained on two public fish datasets - DeepFish (Garcia-D'Urso et al., 2022) and OzFish (AIMS et al., 2019) both modified to only include class 'fish' and not to a species level. The final step was the fine-tuning on DATA<sub>E</sub>. The training was performed on a high-performance GPU cluster (NVIDIA A40 PCIe GPU) for a fixed 150

epochs. Convergence was attained at epoch 125 with an optimal mAP across Intersection over Union (IoU) thresholds ranging from 0.5 to 0.95 (increment steps of 0.05) on the validation set. The training lasted around 11 h. The processing pipeline for DL detection is depicted in Fig. 2.

**2.5. Evaluation metrics**

The two different experiments require different metrics to evaluate them. The first experiment follows the assessment of the YOLO model trained beforehand. The second experiment will look at the binary transect-wise assessment of DATA<sub>E</sub> and comparing the DL model in comparison to the manual video counting.

**2.5.1. Deep learning model evaluation**

To find an overall indication of the prediction capability of the DL model, the mean average precision was used, which consists of the area under the curve (AUC) of the precision-recall curve (Eq. 1). Precision (Eq. 2) indicates the proportion of correct detections among all detections made by the model, while recall (Eq. 3) represents the proportion of actual correct detections that the model successfully identified. The F1 score is a harmonic average that combines the precision and recall into one metric (Eq. 4). These three metrics are common metrics used to evaluate the performance of DL models on a given

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** The video processing pipeline for the method evaluation.</figcaption></figure>
task. The abbreviations FN, FP, TN and TP refer to False Negatives, False Positives, True Negatives and True Positives respectively.

$$meanAveragePrecision = mAP = \frac{1}{n} \sum_{k=1}^{k=n} AP_k \qquad (1)$$

$$Precision = \frac{TP}{TP + FP} \qquad (2)$$

$$Recall = \frac{TP}{TP + FN} \qquad (3)$$

$$F1 Score = \frac{2*TP}{2*TP + FN + FP} \qquad (4)$$

### 2.5.2. Binary evaluation of automated and manual video data

To verify the feasibility of using a DL model in a simplified binary task of presence and absence, the following metrics were calculated to compare the presence/absence table generated by the pipeline with the manual video counting. The accuracy (Eq. 5), sensitivity (Eq. 6), specificity (Eq. 7), 1-specificity (Eq. 8), Cohens Kappa (Eq. 9, Landis and Koch, 1977) and true skill statistic (TSS, Eq. 10) are used to get a holistic insight into how the model performs on this specific task. Cohens Kappa is used to evaluate the inter-reliability between two or more measuring methods, in this case between the manual video counting and the DL predictions. The TSS corrects for the dependence of Cohens Kappa on its prevalence (Allouche et al., 2006).

$$Accuracy = \frac{TP + TN}{TP + FP + TN + FN} \qquad (5)$$

$$Sensitivity = \frac{TP}{TP + FN} \qquad (6)$$

$$Specificity = \frac{TN}{TN + FP} \qquad (7)$$

$$1 - Specificity = 1 - \frac{TN}{TN + FP} = \frac{FP}{TN + FP} \qquad (8)$$

$$Cohens Kappa = \frac{(TP + FP)(TP + FN) + (FN + TN)(TN + FP)}{(TP + FP + TN + FN)^2} \qquad (9)$$

$$TSS = Sensitivity - Specificity - 1 \qquad (10)$$

## 3. Results

### 3.1. Deep learning model

Fig. 3 presents the confusion matrix for the 20 classes of fish. Background FPs refer to incorrect detections by the DL model, which identified parts of the background as objects, while background FNs refer to missed detections against the background. Darker coloration indicates a higher relative classification in this specific true class. This can suggest either higher overall accuracy (when it is on the diagonal, indicating more correct predictions) or higher misclassification rates (when it is off the diagonal, indicating more instances incorrectly classified). The confusion matrix shows a clear diagonal line for most of the species covered except, for the classes *Mullus surmuletus* and *Other*. Overall a mean average precision (mAP) of 0.56 is achieved. The model has a precision at 50 % confidence threshold (a value that represents the

<figure><img src="image_6.png" /><figcaption>Fig. 3. The confusion matrix of the YOLOv7 model with confidence threshold of 50 %, presenting the relation between Predicted and True classes in absolute rather than relative values.</figcaption></figure>
minimum level of certainty required for the model to classify an object) of 0.78 with 17,415 TPs and 4919 FPs. Individual species precision (Table S4) values range from 0.17 (class’Other’) to 0.94 (class’-Sarpa salpa). Second worst after the ‘Other’ class is ‘Seriola’ with a precision of 0.49. Recall values at 50 % confidence (overall 0.60) range from 0.22 for *M. surmuletus* to 0.81 for *Epinephelus marginatus*.

Three different examples of images and the corresponding YOLO model detections are presented in Fig. 4. Original extracted frames without any preprocessing are presented in Fig. 4-A, whilst 4-B depicts preprocessed images with the corresponding detections. Two images are of rocky substrate with a variety of different species seen in the area (primarily *S. salpa* and *Sciaena umbra*) and the third image shows an artificial reef with a passing school of *Diplodus vulgaris*. Partial Figures in 4-C are framed as correctly detected species in green and missed detections in red.

## 3.2. Binary presence/absence task evaluation

For the second experiment, the experimental dataset (DATA<sub>E</sub>) was used. The model detections in this dataset were formatted into a presence/absence table that showed the detection or nondetection of each species in each transect. The presence/absence per species per transect (referred to as a species-transect pair) and overall (all transects combined) were assessed using the different binary metrics (accuracy, sensitivity and specificity). Fig. 5 shows the relation between the sensitivity and 1-specificity of the model in predicting the presence/absence of the 20 classes. This graph shows an area under the curve (AUC) of 0.93, indicating the model performs well in the presence/absence task.

The AUC was also calculated individually for each species (Fig. 6),

<figure><img src="image_7.png" /></figure>

Fig. 5. The sensitivity (blue line) is plotted against 1-specificity, determined with intervals of 0.05 in confidence threshold, making it possible to extract the AUC, which reflects the overall performance of the model in predicting the species-transect-pairs overall. The AUC value is indicated in the bottom right corner and the dashed red line corresponds to a random classification. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

and ranges from 0.36 (*Serranus cabrilla*) to 0.99 (*D. vulgaris*, *Seriola sp.*, *Thalassoma pavo* and *Apogon imberbis*). An AUC of 0.00 (*S. umbra* and *Sparus aurata*) indicates that there was no observation of this species in any transect and these are therefore ignored. Not all species curves behave the same way; some species are easier while others are harder to detect (*M. surmuletus*) or more elusive (*S. cabrilla*).

The same metrics (accuracy, sensitivity and specificity) are presented in relation to the confidence threshold in Fig. 7, which gives a better indication of how the detection confidence has an impact on the corresponding model metric. Any detection with a confidence lower than

<figure><img src="image_8.png" /></figure>

Fig. 4. Example raw images of the test dataset (A). Preprocessing and corresponding detections (B). Column C corresponds to good detections (framed in green) and missed detections (framed in red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** The sensitivity (blue line) is plotted against 1-specificity, determined with intervals of 0.05 in confidence threshold, making it possible to extract the AUC for each species, which reflects the performance of the model in predicting that species in the transects. These curves indicate how well the model is able to grasp the species richness of the transects for each of the species investigated. The AUC value is indicated in the bottom right corner and the dashed red line corresponds to a random classification. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Three different metrics - accuracy (red), sensitivity (green) and specificity (blue) - are plotted against the confidence thresholds. This shows the influence of choosing different thresholds for the transect evaluation. The purple dashed lines indicate the loose, balanced and strict thresholds. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

the threshold will be disregarded and not incorporated into the subsequent evaluation, increasing potential FN but also decreasing FP. This threshold can and should be chosen according to the task and the requirements of the project. Purple dashed lines in Fig. 7 indicate the different thresholds chosen to examine the impact on the transect evaluation. A loose threshold of 0.05 ensures the possible maximum of individuals are detected, a threshold of 0.60 balances the proportion of FPs and FNs (the crossing point of sensitivity and specificity) and 0.80 maximizes the number of TPs that are included, while excluding FPs from the analysis.

Table 2 presents the metrics, depending on the threshold chosen.

<caption>**Table 2**</caption>

Evaluation metrics for all the transects for the thresholds chosen. Bold numbers indicate the highest values for each metric.

<table><thead><tr><td>Threshold</td><td>Accuracy</td><td>Sensitivity</td><td>Specificity</td><td>Cohens Kappa</td><td>TSS</td></tr></thead><tbody><tr><td>Loose (0.05)</td><td>0.64</td><td><strong>0.99</strong></td><td>0.50</td><td>0.34</td><td>0.48</td></tr><tr><td>Balanced (0.60)</td><td>0.86</td><td>0.84</td><td>0.84</td><td>0.64</td><td><strong>0.68</strong></td></tr><tr><td>Strict (0.80)</td><td><strong>0.90</strong></td><td>0.72</td><td><strong>0.94</strong></td><td><strong>0.69</strong></td><td>0.67</td></tr></tbody></table>
Accuracy values range from 0.64 to 0.90, sensitivity from 0.99 to 0.72 and specificity from 0.50 to 0.94 for the different thresholds. Accuracy and specificity are highest with the strict threshold while sensitivity is highest in the loose threshold. The stricter the threshold the higher the specificity, while the sensitivity increases with a looser threshold. Cohens Kappa is highest for the strict threshold and TSS is highest for the balanced setting with values of 0.69 for Cohens Kappa and 0.68 for TSS respectively.

The three different methods (diver data, manual video data, automated video data with three different thresholds) were evaluated using colour codes (Fig. 8), where red indicates FNs, yellow indicates FPs and orange indicates TPs. White indicates TNs. Squares marked with a cross are species seen by the diver in the transect during the UVC. The DL detections were double checked for the strict case to ensure the

individual in question were correctly identified and classified. Transects 1 to 39 are over seagrass meadows (Posidonia oceanica) while transects 39 to 64 are over rocky substrates. Comparing the two DL-independent methods, the average species richness per site over rocky substrates is 7.88 for the manual video count and 9.12 for the traditional UVC. Values over seagrass meadows are lower, average species richness of 3.89 (manual video count) and 5.05 (traditional UVC).

For the DL detections, the loose threshold (Fig. 8 - A) gives a total of 450 FPs (yellow colour) with 5 FNs (red colour). In total, 348 species-transect pairs were correctly predicted (TP) and 477 were correctly not predicted (TN). The model performed worst for the species *M. surmuletus*, which was missed in 2 out of the 6 videos in which it was present (FN), while being predicted 24 times it was absent (FP). This result is consistent with the confusion matrix of the model evaluation

<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Transect evaluation for the different thresholds: A, B and C are loose, balanced and strict thresholds respectively. Successful detections are shown in orange, missed species (FN) are shown in red and falsely detected species (FP) are shown in yellow. Crosses indicate the species was observed in the transect by the diver doing the UVC. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
(Fig. 3), which shows 121 detections of *M. surmuletus* were missed. The different substrates are not distinguishable in the loose threshold case. The predicted average species richness is 15.5 over rocky substrates and 10.39 over seagrass meadows. The species richness is thus overestimated by 7.62 species for rocky substrates and 6.5 for seagrass meadows.

The balanced threshold model configuration (Fig. 8 - B) gives 135 FPs, 41 FNs, 312 TPs and 792 TNs. Out of 1280 possible predictions, 1104 were predicted correctly.

*M. surmuletus* has been seen by the DL model in 1 of the 6 videos in which it was present (TP) and 3 in which it was not (FP). The predicted species richness over rocky substrates is 10.54 and the species richness over seagrass meadows is 4.68. There is a clear difference observable - qualitatively and quantitatively - between the two substrate types. Differences in species richness between the automated and manual count are 2.66 for the rocky substrates and 0.79 for the seagrass meadows.

For the strict threshold (Fig. 8 - C), the results were 45 FPs, 79 FNs, 274 TPs and 882 TNs. A total of 1156 species-transect pairs were correctly predicted. For the species,

*M. surmuletus* the strict threshold gave the best model performance, with 1 TP and only 1 FP. For the strict case, the predicted species richness over rocky substrates and seagrass meadows are 8.04 and 3.06, respectively. The difference in species richness between the DL and the manual count is thus smaller with this threshold, with species richness differences of -0.83 over seagrass substrates and 0.16 over rocky substrates. The strict threshold is the only threshold that differs by less than 1 over both substrates compared to the manual video count but it underestimates the average seagrass richness.

### 3.3. Method comparison

The multi-dimensional, quasi-proportional Venn diagrams from the R package *nVennR* (Pérez-Silva et al., 2018) give a qualitative insight into the different species richness of the sites as measured by all the different techniques and the level of agreement between them. They are presented in Figs. 9 and 10 for the Cap Roux and Corniche Varoise sites, respectively. Depending on availability of video recordings and diver

data, 3 to 6 transects were concatenated *per* site, meaning the transects were combined to assess the site-wide fish diversity. For the DL method, the strict confidence threshold was chosen, because it had high accuracy (1156 species-transect pairs were correctly predicted) along with a substantial Cohens Kappa, excellent TSS values and better species richness prediction than the other thresholds.

From the point of view of assessing the model, the yellow and green-yellow colours indicate FP predictions since these species were not seen in the video but were detected by the DL model. Distinct red and red-green colour indicate FN since the species were present in the video but not detected by the model. The yellow-red and all three colours overlapping indicate TPs. A distinct green colour shows species that were only seen by the diver during the UVC and so will not contribute to the evaluation of the model but will be used to give an idea of how the different methods all contribute to build a picture of site diversity. A total of 26 FNs and 16 FPs were predicted along with 94 TPs and 144 TNs, forming a total of 280 species-site pairs.

For the location Cap Roux (Fig. 9), an overall agreement can be observed between the methods concerning the species richness. The majority of species were counted in each site with all of the methods. The FP (yellow and green-yellow) range from 0 to 2 with a median of 2 for the DL model (Fig. 9 - b, d and f-i). The green-yellow could reflect human error when evaluating the videos but were double checked to be correctly classified as FP. Species missed in the video (red) were observed in 6 sites (Fig. 9 - a-c, f and i) while on 6 sites (Fig. 9 - a-c, g and h) the diver saw fish species that were not observed in the video (green). In Fig. 9 - d the DL model agreed with the manual video count while the diver missed one species (yellow-red). An overlap between the diver and the manual video count (species missed by the DL model) occurred in 6 sites (Fig. 9 - a, c, d, f, g and h).

The average overlapping species richness over seagrass meadows at all depths is 4.6 while over rocky habitats it is 9.5.

For the second location in Corniche Varoise (Fig. 10) the video transects do not identify species that are unseen by the diver. However, in 4 of the 5 sites (Fig. 10 - b-e) several species were only seen by the diver, with the number of missed species ranging from 1 to 5 with a

<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Venn diagrams at the site coordinates (green sites (a-e) are seagrass meadows, red sites (f-i) are rocky substrates) showing the overlap between detection methods in different sites at the location **Cap Roux** (Image credit Google Maps 2024). Overlapping colours indicate a common species pool whilst distinct colours indicate detection by a specific method. Yellow and yellow-green colours are indicating FPs and red and red-green colours indicate FNs. The colours red-yellow and all colours indicate TPs. Green means the video did not sufficiently cover all species in the transect and these species were only seen by the diver. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Venn diagrams at the site coordinates (green sites (a-c) are seagrass meadows, red sites (d-e) are rocky substrates) showing the overlap of between detection methods in different sites at the location *Corniche Varoise* (Image credit Google Maps 2024). Overlapping colours indicate a common species pool whilst distinct colours indicate detection by a specific method. The yellow and yellow-green colour indicate FPs and red and red-green colours indicate FNs. The colours red-yellow and all colours indicate TPs. Green means the video did not sufficiently cover all species in the transect and species were only seen by the diver. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

median of 2. There are FPs for the DL model at all sites ranging from 1 to 3 with a median of 1. Compared to Cap Roux higher average FP and lower number of video-contributed species are observable.

Overall the seagrass meadow sites have a species richness of 6.5 and the rocky substrate sites have a species richness of 9.5.

# 4. Discussion

Overall, this study demonstrates the successful incorporation of a DL model into the process of assessing and validating biodiversity at two different ecologically important sites on the French Riviera. The proposed model is able to correctly detect the presence or absence of the majority of the 19 different fish species in each transect. This makes it a valid addition to a holistic approach and increases the robustness of justifications for the implementation of MPAs. The automation of the video analysis will decrease processing time and allow more time for interpretation and human-dependent decision-making, thus increasing the efficiency of the whole procedure. This highlights the importance of utilising DL tools in applied scenarios and that they simplify the task of evaluating transect-based videos obtained by divers.

## 4.1. *Choice of deep learning model*

For the deep learning aspect, when compared to studies with stationary cameras (Jalal et al., 2020; Knausgård et al., 2022), the present study using moving cameras underperforms, due to the challenge of data collection and movement of the camera. A study on moving cameras that is more comparable to the present study is Connolly et al., 2022, which used an ROV to collect videos in which they detected two fish species and achieved precision values of 0.28 to 0.96 and recall values of 0.46 to 0.80. These values agree with this study's precision (0.17 to 0.94) and recall (0.22 to 0.81) values on different species. Other studies have a

more constrained environment and achieve very inspiring results in for example re-identifying individuals (Olsen et al., 2023) that could be very interesting for future collaborative work, for example concerning *E. marginatus* due to its big size and importance as a top predator and protected species. Marine biologists rely on fast and efficient processing of their data, which is enabled with this DL model.

## 4.2. *Binary presence/absence task evaluation*

The pipeline reached AUC values of over 0.80 for most species, which seems sufficient to detect their presence/absence in a transect. However, the method has some limitations, because the detection rates of certain species are insufficient to confidently predict the presence of the species in a transect - for example *M. surmuletus*, which is an economically important species in this region, has an AUC value of 0.72. Furthermore, the AUC may not always reflect the model's true performance. For example, *A. imberbis* shows great coverage in the transect analysis with an AUC value of 0.99. This suggests that the model is predicting the species *A. imberbis* almost perfectly which is not necessarily the case. The species *A. imberbis* only appeared in 2 transects and was detected in this transect at most confidence thresholds, which implies a sensitivity of 1. Lower thresholds result in some FP and therefore a lower specificity, but because the sensitivity is so high, the AUC remains close to 1. In our case several species fall into this low-occurrence category, for example *Anthias anthias*, *E. marginatus*, *Seriola sp.* and *S. cabrilla* and therefore should not be evaluated with the AUC. Having an imbalance between TP and TN can lead to misinterpretation of the model and so other metrics should be incorporated (Lobo et al., 2008). For example the Cohens Kappa gives a better insight into the overall inter-reliability between two measurement methods and the degree to which they agree with each other, but it suffers from prevalence bias. The TSS metric on the other hand is free of this bias and should be considered as a metric to asses the
model. In our results, the highest value for the Cohens Kappa is 0.69, which is considered substantial (Landis and Koch, 1977), and the highest True Skill Statistic (TSS) value of 0.68 is considered excellent (Kقام et al., 2016). These two metrics show that there is a high degree of agreement between the manual and automated methods, providing evidence that manual video inspection could be replaced with a DL algorithm.

4.3. Method comparison

Following our assessment of the confidence thresholds, the method-
specific species evalua- tion was conducted with the strict threshold. It
showed the highest accuracy (0.90) as well as the best Cohens Kappa
(0.69) and second-best TSS (0.67). With this in mind, there will be a
large number of FNs but also a substantially reduced number of FPs,
which is important to correctly estimate species richness on transects. It
is important to examine two cases: first UVC compared with manual
video transects; and second, how the use of the DL model changes the
observed species richness. In the first case, in the Corniche Varoise sites
(Fig. 10), there are only added species from the traditional UVC which
highlights the importance of expert knowledge (Cappo et al., 2003).
However, the manual video count added species at Cap Roux. On the other
hand, the diver's head has free movement and is only limited by the
theoretical area imposed by the protocol, while the camera's point of
view is inherently fixed. This means that a diver can overcome the
spatial limitations in a way the camera cannot, and could explain the
added species in some Corniche Varoise sites. These differences can have
a small but significant impact on whether a species is sighted in a
transect or not - with either methods. The impact will be greater on rarer
species since they may be missed more easily and not reappear later in
the transect. Therefore, protocols should be adapted to address these
differences, and could, for example, involve using multiple cameras, one
facing straight ahead and the other looking down at the seabed to cover
both semi-pelagic and benthic species, and so more closely observe what
the diver observes. Another protocol modification could be to increase
the area covered by DOV transects to enable a more equal comparison
with the UVC. Further research needs to be conducted to find the correct
protocol parameters for fair comparison. In the second case, we examine
the performance of the automated video transect evaluation. While
other studies have shown successful automated evaluation of stationary
or baited videos (Jalal et al., 2020; Muksit et al., 2022; Villon et al.,
2018), the present study utilised depth-variable DOV of transects. As
noted in Villon et al., 2018 and Connolly et al., 2022, this is an important
use-case of artificial intelligence in marine conservation that should be
the focus of increased research in the future. Analysing transect videos
automatically using a high-throughput DL model, instead of manual
inspection, maintains a good overall level of quality while minimising
analysis time. A total of 85 % of the species-site pairs were correctly
detected, highlighting the feasibility of this approach while eliminating
the diver bias, which can contribute an uncertainty of up to 25 % of the
local species diversity. The DL model studied is able to provide a true
picture and, in the future, this pipeline could help to gather more bias-
free data for MPA managers to streamline the process of implementing
and evaluating MPAs, making the decision-making process faster and
more efficient.

4.4. Additional ecological information

In addition to collecting biodiversity data and monitoring its evo-
lution over time, other ecological information can be extracted using
this simple and comprehensive pipeline, which is valuable for the
establishment of protected areas or no take zones. While seagrass
meadows are known for nursery and living habitats (Boudouresque
et al., 2021; Franco et al., 2006), several studies have shown that the
species richness is higher over rocky substrates than over P. oceanica
(Cheminée et al., 2021). This difference was also observed in the present
study for the focused species list that the DL model was trained on. The
protected species *E. marginatus* was seen by the DL model only over
rocky substrates and occurred twice as often inside of the protected area
than outside. This gives an indication that MPAs can be an important
safe haven for top predator fishes in their hunting and reproduction
grounds and is in alignment with previous studies (Guidetti et al., 2014).
In the case of the two *Diplodus* ssp. the analysis showed that these two
economically important species occur mostly over rocky substrates,
which further highlights the importance of this habitat and how artifi-
cialisation of the rocky shoreline can damage some species more than
others. These results are preliminary and need to be treated with caution
since the data is limited to two sites and one time frame. However, this
pipeline will be applied to more data collections and will generate in-
formation about different ecosystems, sites and time frames creating a
standardised procedure for the future.

5. Conclusion

This study showed that no method of evaluation (neither video nor
diver data) is perfect but a combination of methods can achieve greater
robustness in evaluating a site. A combinative approach is in agreement
with non-automated studies which looked at different manual methods
(Goetze et al., 2015; Willis and Babcock, 2000). In the case of diver
limitations (Cappo et al., 2003), the video recording protocol needs to be
adapted to produce data that is comparable between methods and create
a strong, robust and reproducible protocol that can be effectively
employed at depths beyond the physiological limits of human divers.
Furthermore, this study showed that deep learning models can help
researchers eliminate expert bias in video analysis, while also saving
time and effort through automation. With the decrease in time spent by
researchers on processing the data, data collection missions can be
carried out more often, increasing the temporal and spatial coverage of
species distribution. This proof of concept shows not only a new high-
throughput analysis of diver-operated videos (DOVs) but also suggests
it may be possible to evaluate videos recorded by remote operated ve-
hicles (ROV), which would allow data to be collected in environments
with meteorological, depth-related or logistical constraints.

CRediT authorship contribution statement

**Kilian Bürgi:** Writing – original draft, Investigation, Data curation.
**Charles Bouveyron:** Writing – review & editing, Validation, Supervision.
**Diane Lingrand:** Writing – review & editing, Validation, Supervision.
**Benoît Derijard:** Writing – review & editing, Supervision.
**Frédéric Precioso:** Writing – review & editing, Validation, Supervision.
**Cécile Sabourault:** Writing – review & editing, Supervision, Funding acquisition.

Declaration of competing interest

None.

Acknowledgements

This work was only made possible thanks to collaborations with the
projects RECIF and FishHealth funded by FEAMPA (Fonds européen
pour les affaires maritimes, la pêche et l'aquaculture) and with the divers who provided the UVC- and video data from the corresponding field campaigns. The authors are grateful to the OPAL infrastructure from Université Côte d'Azur for providing resources and support. We would also like to thank Catherine for the proofreading of this manuscript and her feedback. This project was funded through the UCAJEDI Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-15-IDEX-01 (MARE project) and through 3IA@cote d'azur - ANR-19-P3IA-0002.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecoinf.2024.102959.

### Data availability

Codes and Scripts are made available on GitHub: https://github.com/PiSuMp/autoUC_MedSea/tree/main

DataT and DataE are made available here: https://doi.org/10.5061/dryad.f7m0cfzf6f

## References

AIMS, University of Western Australia, Curtin University, 2019. OzFish Dataset - Machine learning dataset for Baited Remote Underwater Video Stations. https://doi.org/10.25845/e528f062c5097.

Allken, V., et al., 2021a. A deep learning-based method to identify and count pelagic and mesopelagic fishes from trawl camera images. ICES J. Mar. Sci. 78, 3780-3792. https://doi.org/10.1093/icesjms/fsab227.

Allken, V., et al., 2021b. A real-world dataset and data simulation algorithm for automated fish species identification. Geosci. Data J. 8, 199-209. https://doi.org/10.21335/NMDC-551736490.

Allouche, O., Tsoar, A., Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223-1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x.

Boudouresque, C.-F., et al., 2021. Restoration of seagrass meadows in the Mediterranean Sea: a critical review of effectiveness and ethical issues. Water 13, 1034. https://doi.org/10.3390/w13081034.

Caldwell, Z.R., et al., 2016. Reef fish survey techniques: assessing the potential for standardizing methodologies. PLoS One 11, e0153066. https://doi.org/10.1371/journal.pone.0153066.

Cappo, M., et al., 2003. Aquatic protected areas - what works best and how do we know. In: chap. Potential of video techniques to monitor diversity, abundance and size of fish in studies of Marine Protected Areas, pp. 455-464 url: https://books.google.fr/books?id=9_NEAAAAYAAJ.

Carranza, M., et al., 2019. Urban expansion depletes cultural ecosystem services: an insight into a Mediterranean coastline. Rendiconti Lincei. Sci. Fisiche Nat. 31, 103-111. https://doi.org/10.1007/s12210-019-00866-w.

Chemineée, A., et al., 2021. All shallow coastal habitats matter as nurseries for Mediterranean juvenile fish. Sci. Rep. 11, 14631. https://doi.org/10.1038/s41598-021-93557-2.

Colton, M., Swearer, S., 2010. A comparison of two survey methods: differences between underwater visual census and baited remote underwater video. Marine Ecol. Prog. Series (MEPS) 400, 19-36. https://doi.org/10.3354/meps08377.

Connolly, R.M., et al., 2022. Fish surveys on the move: adapting automated fish detection and classification frameworks for videos on a remotely operated vehicle in shallow marine waters. Front. Mar. Sci. 9. https://doi.org/10.3389/fmars.2022.918504.

Demirel, N., Zengin, M., Ulman, A., 2020. First large-scale eastern mediterranean and black sea stock assessment reveals a dramatic decline. Front. Mar. Sci. 7. https://doi.org/10.3389/fmars.2020.00103.

Doney, S.C., et al., 2012. Climate change impacts on marine ecosystems. Annu. Rev. Mar. Sci. 4, 11-37. https://doi.org/10.1146/annurev-marine-041911-111611.

Franco, A., et al., 2006. Use of shallow water habitats by fish assemblages in a Mediterranean coastal lagoon. Estuar. Coast. Shelf Sci. 66, 67-83. https://doi.org/10.1016/j.ecss.2005.07.020.

Garcia-D'Urso, N., et al., 2022. The DeepFish computer vision dataset for fish instance segmentation, classification, and size estimation. Sci. Data 9, 287. https://doi.org/10.1038/s41597-022-01416-0.

Girshick, R., 2015. Fast R-CNN. In: In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448. https://doi.org/10.1109/ICCV.2015.169.

Gissi, E., et al., 2021. A review of the combined effects of climate change and other local human stressors on the marine environment. Sci. Total Environ. 755, 142564. https://doi.org/10.1016/j.scitotenv.2020.142564.

Goetze, J.S., et al., 2015. Diver operated video most accurately detects the impacts of fishing within periodically harvested closures. J. Exp. Mar. Biol. Ecol. 462, 74-82. https://doi.org/10.1016/j.jembe.2014.01.004.

Gorrud-Colvert, K., et al., 2021. The MPA guide: a framework to achieve global goals for the ocean. Science 373, eabf0861. https://doi.org/10.1126/science.abf0861.

Guidetti, P., et al., 2014. Large-scale assessment of Mediterranean marine protected areas effects on fish assemblages. PLoS One 9, e91841. https://doi.org/10.1371/journal.pone.0091841.

Harmelin-Vivien, M.L., et al., 1985. Evaluation visuelle des peuplements et populations de poissons méthodes et problèmes. Rev. Ecol. (La Terre et La Vie) 40, 467-539. https://doi.org/10.3406/revec.1985.5297.

Jalal, A., et al., 2020. Fish detection and species classification in underwater environments using deep learning with temporal information. Eco. Inform. 57, 101088. https://doi.org/10.1016/j.ecoinf.2020.101088.

Knausgård, K., et al., 2022. Temperate fish detection and classification: a deep learning based approach. Appl. Intell. 52, 6988-7001. https://doi.org/10.1007/s10489-020-02154-9.

Komac, B., et al., 2016. Modelization of the current and future habitat suitability of Rhododendron ferrugineum using potential snow accumulation. PLoS One 11, e0147324. https://doi.org/10.1371/journal.pone.0147324.

Kulbicki, M., Sarramégna, S., 1999. Comparison of density estimates derived from strip transect and distance sampling for underwater visual censuses: a case study of Chaetodontidae and Pomacanthidae. Aquat. Living Resour. 12, 315-325. https://doi.org/10.1016/S0990-7440(99)00116-3.

Landis, J.R., Koch, G.G., 1977. The measurement of observer agreement for categorical data. Biometrics 33, 159-174. https://doi.org/10.2307/2529310.

Lin, T.-Y., Maire, M., et al., 2014. Microsoft coco: Common objects in context. In: Computer Vision-ECCV 2014: 13th European Conference. Springer, Zurich, Switzerland, pp. 740-755. https://doi.org/10.1007/978-3-319-10602-1_48.

Lin, T.-Y., Goyal, P., et al., 2017. Focal loss for dense object detection. In: CoRR abs/1708.02002. https://doi.org/10.48550/arXiv.1708.02002.

Lobo, J.M., Jiménez-Valverde, A., Real, R., 2008. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145-151. https://doi.org/10.1111/j.1466-8238.2007.00358.x.

Malde, K., et al., 2020. Machine intelligence and the data-driven future of marine science. ICES J. Mar. Sci. 77, 1274-1285. https://doi.org/10.1093/icesjms/fsz057.

Mcclanahan, T., et al., 2007. Influence of instantaneous variation on estimates of coral reef fish populations and communities. Mar. Ecol. Prog. Ser. 340, 221-234. https://doi.org/10.3354/meps340221.

Mclean, D., et al., 2005. A comparison of temperate reef fish assemblages recorded by three underwater stereo-video techniques. Mar. Biol. 148, 415-425. https://doi.org/10.1007/s00227-005-0090-6.

Mejjad, N., Rossi, A., Pavel, A.B., 2022. The coastal tourism industry in the Mediterranean: a critical review of the socio-economic and environmental pressures impacts. Tour. Manag. Perspect. 44, 101007. https://doi.org/10.1016/j.tmp.2022.101007.

Mohamed, H.E.-D., et al., 2020. MSR-YOLO: method to enhance fish detection and tracking in fish farms. Proc. Comp. Sci. 170, 539-546. https://doi.org/10.1016/j.procs.2020.03.123.

Moksit, A.A., et al., 2022. YOLO-fish: a robust fish detection model to detect fish in realistic underwater environment. Eco. Inform. 72, 101847. https://doi.org/10.1016/j.ecoinf.2022.101847.

Myers, R.A., Hutchings, J.A., Barrowman, N.J., 1997. Why do fish stocks collapse? The example of cod in Atlantic Canada. Ecol. Appl. 7, 91-106. https://doi.org/10.2307/2269409.

Olsen, Ø.L., et al., 2023. A contrastive learning approach for individual re-identification in a wild fish population. https://doi.org/10.48550/arXiv.2301.00596.

Park, J.-H., Kang, C., 2020. A study on enhancement of fish recognition using cumulative mean of YOLO network in underwater video images. J. Marine Sci. Eng. 8. https://doi.org/10.3390/jmse8110952.

Pérez-Silva, J.G., Araujo-Voces, M., Quesada, V., 2018. nVenn: generalized, quasi-proportional Venn and Euler diagrams. Bioinformatics 34, 2322-2324. https://doi.org/10.1093/bioinformatics/bty109.

Priyankan, K., Fernando, T.G.I., 2021. Mobile application to identify fish species using YOLO and convolutional neural networks. In: In: Proceedings of International Conference on Sustainable Expert Systems. Springer Singapore, pp. 303-317. https://doi.org/10.1007/978-981-33-4355-9_24.

Ren, S., et al., 2016. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031.

Rubbens, P., et al., 2023. Machine learning in marine ecology: an overview of techniques and applications. ICES J. Mar. Sci. 80, 1829-1853. https://doi.org/10.1093/icesjms/fsad100.

Smale, D., et al., 2019. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Chang. 9, 306-312. https://doi.org/10.1038/s41558-019-0412-1.

Spampinato, C., et al., 2016. Fine-grained object recognition in underwater visual data. Multimed. Tools Appl. 75, 1701-1720. https://doi.org/10.1007/s11042-015-2601-x.

Vabø, R., et al., 2021. Automatic interpretation of salmon scales using deep learning. Eco. Inform. 63, 101322. https://doi.org/10.1016/j.ecoinf.2021.101322.

Vasilakopoulos, P., Maravelias, C.D., Tserpes, G., 2014. The alarming decline of Mediterranean fish stocks. Curr. Biol. 24, 1643-1648. https://doi.org/10.1016/j.cub.2014.05.070.

Villon, S., et al., 2018. A deep learning method for accurate and fast identification of coral reef fishes in underwater images. Eco. Inform. 48, 238-244. https://doi.org/10.1016/j.ecoinf.2018.09.007.

Wang, Y., et al., 2021, UIEC²-net: CNN-based underwater image enhancement using two color space. Signal Process. Image Commun. 96, 116250. https://doi.org/10.1016/j.image.2021.116250.
Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M., 2023. YOLOv7: Trainable bag-of- freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/ CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475. https://doi.org/10.48550/arXiv.2207.02696.

Wang, A. et al. (2024). “Yolov10: Real-Time End-to-End Object Detection”. In: arXiv preprint arXiv:2405.14458. doi: 10.48550/arXiv.2405.14458.

Willis, T., Babcock, R., 2000. A baited underwater video system for the determination of relative density of carnivorous reef fish. Mar. Freshw. Res. 51, 755–763. https://doi.org/10.1071/MF00010.

Wu, Y., et al., 2019. Detectron2. https://github.com/facebookresearch/detectron2.

Wu, C.M., et al., 2022. Underwater trash detection algorithm based on improved YOLOv5s. J. Real-Time Image Proc. 19, 911–920. https://doi.org/10.1007/s11554-022-01232-0.

Xu, W., Mattzner, S., 2018. Underwater fish detection using deep learning for water power applications. In: 5th Annual Conf. on Computational Science & Computational Intelligence (CSCI’18). https://doi.org/10.1109/CSCI46756.2018.00067.