An Efficient Method for Underwater Video Summarization and Object Detection Using YoLoV3

Currently,worldwide industries and communities are concerned with building,expanding,and exploring the assets and resources found in the oceans and seas.More precisely,to analyze a stock,archaeology,and surveillance,sev-eral cameras are installed underseas to collect videos.However,on the other hand,these large size videos require a lot of time and memory for their processing to extract relevant information.Hence,to automate this manual procedure of video assessment,an accurate and efficient automated system is a greater necessity.From this perspective,we intend to present a complete framework solution for the task of video summarization and object detection in underwater videos.We employed a perceived motion energy(PME)method tofirst extract the keyframes followed by an object detection model approach namely YoloV3 to perform object detection in underwater videos.The issues of blurriness and low contrast in underwater images are also taken into account in the presented approach by applying the image enhancement method.Furthermore,the suggested framework of underwater video summarization and object detection has been evaluated on a publicly available brackish dataset.It is observed that the proposed framework shows good performance and hence ultimately assists several marine researchers or scientists related to thefield of underwater archaeology,stock assessment,and surveillance.

Low Bit Rate Underwater Video Image Compression and Coding Method Based on Wavelet Decomposition

In view of the limited bandwidth of underwater video image transmission,a low bit rate underwater video compression coding method is proposed.Based on the preprocessing process of wavelet transform and coefficient down-sampling,the visual redundancy of underwater image is removed and the computational coefficients and coding bits are reduced.At the same time,combined with multi-level wavelet decomposition,inter frame motion compensation,entropy coding and other methods,according to the characteristics of different types of frame image data,reduce the number of calculations and improve the coding efficiency.The experimental results show that the reconstructed image quality can meet the visual requirements,and the average compression ratio of underwater video can meet the requirements of underwater acoustic channel transmission rate.

Underwater video transceiver designs based on channel state information and video content

Underwater hostile channel conditions challenge video transmission designs. The current designs often treat video coding and transmission schemes as individual modules. In this study, we develop an adaptive transceiver with channel state information(CSI) by taking into account the importance of video components and channel conditions. The design is more effective than the traditional ones. However, in practical systems, perfect CSI may not be available. Therefore, we compare the imperfect CSI case with existing schemes, and validate the effectiveness of our design through simulations and measured channels in terms of a better peak signal-to-noise ratio and a higher video structural similarity index.

Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning

The Norway lobster,Nephrops norvegicus,is one of the main commercial crustacean fisheries in Europe.The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges.The Spanish Oceanographic Institute(IEO)andMarine Institute Ireland(MIIreland)conducts annual underwater television surveys(UWTV)to estimate the total abundance of Nephrops within the specified area,with a coefficient of variation(CV)or relative standard error of less than 20%.Currently,the identification and counting of the Nephrops burrows are carried out manually by the marine experts.This is quite a time-consuming job.As a solution,we propose an automated system based on deep neural networks that automatically detects and counts the Nephrops burrows in video footage with high precision.The proposed system introduces a deep-learning-based automated way to identify and classify the Nephrops burrows.This research work uses the current state-of-the-art Faster RCNN models Inceptionv2 and MobileNetv2 for object detection and classification.We conduct experiments on two data sets,namely,the Smalls Nephrops survey(FU 22)and Cadiz Nephrops survey(FU 30),collected by Marine Institute Ireland and Spanish Oceanographic Institute,respectively.From the results,we observe that the Inception model achieved a higher precision and recall rate than theMobileNetmodel.The best mean Average Precision(mAP)recorded by the Inception model is 81.61%compared to MobileNet,which achieves the best mAP of 75.12%.

Fish assemblages in protected seagrass habitats:Assessing fish abundance and diversity in no-take marine reserves and fished areas

Marine reserves are an important management tool for conserving local biodiversity and protecting fragile ecosystems such as seagrass that provide significant ecological functions and services to people and the marine environment.With humans placing ever-growing pressure on seagrass habitats,marine reserves also provide an important reference from which changes to seagrass and their ecological assemblages may be assessed.After eight years of protection of seagrass beds(Posidonia australis)in no-take marine reserves(Sanctuary Zones)within the Jervis Bay Marine Park(New South Wales,Australia;zoned in 2002),we aimed to assess what changes may have occurred and assess continuing change through time in fish assemblages within these seagrass meadows.Using baited remote underwater videos(BRUVs),we sampled seagrass fish assemblages at three locations in no-take zones and five locations in fished zones three times from 2010 to 2013.Overall,we observed a total of 2615 individuals from 40 fish species drawn from 24 families.We detected no differences in total fish abundance,diversity,or assemblage composition between management zones,although we observed a significant increase in Haletta semifasciata,a locally targeted fish species,in no-take marine reserves compared with fished areas.Fish assemblages in seagrass varied greatly amongst times and locations.Several species varied in relative abundance greatly over months and years,whilst others had consistently greater relative abundances at specific locations.We discuss the potential utility of marine reserves covering seagrass habitats and the value of baseline data from which future changes to seagrass fish populations may be measured.

Assessment of faunal communities and habitat use within a shallow water system using non-invasive BRUVs methodology

Seagrass and mangrove habitats have long been established as critical for diverse species at various life-stages,particularly as nursery grounds.However,despite their intrinsic and environmental value,these ecosystems are increasingly threatened by anthropogenic activities.In Bimini,Bahamas,where ongoing development threatens ecosystem integrity,baited remote underwater video surveys(BRUVs)were used to examine faunal communities in both nearshore habitat and a shallow water central lagoon(average depth 1 m).The study assessed species abundances and spatial distribution in a currently unperturbed of the North Bimini Marine Reserve(NBMR).A total of 140 BRUVs,conducted over a 13-month period,recorded 62 species from 27 different families.MaxN was used to assess relative abundances and multivariate analyses(i.e.nMDS,PCA,PERMANOVA)investigated differences in community composition across discrete factors and environmental variables.Boosted Regression Trees(BRTs)were used to explore environmental variables for their uncorrelated influences on recorded species diversity.Findings evidenced the importance of habitat diversity and particularly mangroveadjacent habitat for teleost fishes in Bimini with species diversity and abundance being significantly greater in the mangrove-adjacentdge habitat.Further,the study highlighted differences in environmental conditions between habitat types and the association this had with species diversity,abundance and distribution.Despite the shallow water environment,BRUVs served as a scalable,non-invasive technique to assess community structures within the study site.Results from this study should inform ongoing decision-making processes regarding the protection of the Bimini Islands ecosystem.