Automated Fish Detection in Underwater Images Using Shape‐Based Level Sets

Ravanbakhsh, Mehdi; Shortis, Mark R.; Shafait, Faisal; Mian, Ajmal; Harvey, Euan S.; Seager, James

doi:10.1111/phor.12091

Cited by 62 publications

(28 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Erik Rodner et al [15] This method depends upon the convolutional neural networks that is done on the objects in order to detect and also for the classification of species. Here the removal of background is proposed which are filtered using the binary SVM classifier for the classification of fishes.…”

Section: Related Workmentioning

confidence: 99%

Multi-Scale Fish Segmentation Refinement Using Contour Based Segmentation

Archana

et al. 2021

Advances in Parallel Computing Technologies and Applications

View full text Add to dashboard Cite

Image processing and the analysis techniques are the increasing attention when they have enabled the non-extractive and the non-lethal approach for the collection of fisheries data. The data collection includes the following requirements such as fish size, catch estimation, regulatory compliance, species recognition and population counting. The main process that is used to measure the size of fish accurately is image segmentation. The challenges that can affect the segmentation of images include the blurring of the image areas due to the water droplets on the camera lens and the fish bodies which are out of the camera view. This project describes the automatic segmentation of fish for underwater images This segmentation algorithm implemented for identify the shape of the fish contour-based segmentation is implemented in this project. The project describes about the issues with an effective contour-based segmentation from an initial segmentation. The refinement is processed from coarse level to fine level. At the coarse level, the entire fish is aligned for the contour of the initial segmentation with trained representative contours by using iteratively reweighted least squares (IRLS). At finer levels, the refinement of contour segments is done to represent poorly segmented or missing shape parts. This method addresses the problems listed above and generates promising results with highly robust segmentation performance and length measurement.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-Scale Fish Segmentation Refinement Using Contour Based Segmentation

Archana

et al. 2021

Advances in Parallel Computing Technologies and Applications

View full text Add to dashboard Cite

show abstract

“…In addition, adaptive control provides an ability to re-adjust the controller parameters online to achieve the required performance when the process parameters are unknown and vary over time [35]. In contrast to the motion control of fully actuated AUVs, the main concern in the design of controllers for underactuated AUVs is that the number of their independent actuators is fewer than degrees of freedom [36]. This feature increases the degree of complexity in the design of nonlinear tracking controllers for such systems.…”

Section: Introductionmentioning

confidence: 99%

AUV Trajectory Tracking Models and Control Strategies: A Review

2021

JMSE

View full text Add to dashboard Cite

Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

show abstract

“…Recent research in automated analysis of underwater videos has shown promising results for detection, counting, and species identification of fish of interest in partially or fully uncontrolled environments (Charalampidis et al, 2012;Boom et al, 2014;Ravanbakhsh et al, 2015;Salman et al, 2016;Shafait et al, 2016;Shortis et al, 2016). However, in general, these systems are not capable of fully automated operation or do not attempt length measurement, as they have been developed for single camera operations with the primary aim of identifying and counting species of interest.…”

Section: Introductionmentioning

confidence: 99%

Towards automating underwater measurement of fish length: a comparison of semi-automatic and manual stereo–video measurements

Harvey

Shortis

Mian

et al. 2017

ICES Journal of Marine Science

Self Cite

View full text Add to dashboard Cite

Underwater stereo–video systems are widely used for counting and measuring fish in aquaculture, fisheries, and conservation management. Length measurements are generated from stereo–video recordings by a software operator using a mouse to locate the head and tail of a fish in synchronized pairs of images. This data can be used to compare spatial and temporal changes in the mean length and biomass or frequency distributions of populations of fishes. Since the early 1990s stereo–video has also been used for measuring the lengths of fish in aquaculture for quota and farm management. However, the costs of the equipment, software, the time, and salary costs involved in post processing imagery manually and the subsequent delays in the availability of length information inhibit the adoption of this technology. We present a semi-automatic method for capturing stereo–video measurements to estimate the lengths of fish. We compare the time taken to make measurements of the same fish measured manually from stereo–video imagery to that measured semi-automatically. Using imagery recorded during transfers of Southern Bluefin Tuna (SBT) from tow cages to grow out cages, we demonstrate that the semi-automatic algorithm developed can obtain fork length measurements with an error of less than 1% of the true length and with at least a sixfold reduction in operator time in comparison to manual measurements. Of the 22 138 SBT recorded we were able to measure 52.6% (11 647) manually and 11.8% (2614) semi-automatically. For seven of the eight cage transfers recorde,d there were no statistical differences in the mean length, weight, or length frequency between manual and semi-automatic measurements. When the data were pooled across the eight cage transfers, there was no statistical difference in mean length or weight between the stereo–video-based manual and semi-automated measurements. Hence, the presented semi-automatic system can be deployed to significantly reduce the cost involved in adoption of stereo–video technology.

show abstract

Automated Fish Detection in Underwater Images Using Shape‐Based Level Sets

Cited by 62 publications

References 28 publications

Multi-Scale Fish Segmentation Refinement Using Contour Based Segmentation

Multi-Scale Fish Segmentation Refinement Using Contour Based Segmentation

AUV Trajectory Tracking Models and Control Strategies: A Review

Towards automating underwater measurement of fish length: a comparison of semi-automatic and manual stereo–video measurements

Contact Info

Product

Resources

About