A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery

With the evolution of the convolutional neural network (CNN), object detection in the underwater environment has gained a lot of attention. However, due to the complex nature of the underwater environment, generic CNN-based object detectors still face challenges in underwater object detection. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates. To tackle this challenge, we propose a detection refinement algorithm based on spatial–temporal analysis to improve the performance of generic detectors by suppressing the false positives and recovering the missed detections in underwater videos. In the proposed work, we use state-of-the-art deep neural networks such as Inception, ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus burrows from underwater videos. Nephrops is one of the most important commercial species in Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms. To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz. From experiment results, we demonstrate that the proposed framework effectively suppresses false positives and recovers missed detections obtained from generic detectors. The mean average precision (mAP) gained a 10% increase with the proposed refinement technique.Versión del edito

Automatic Detection of Nephrops norvegicus Burrows in Underwater Images Using Deep Learning

Autonomous Underwater Vehicles and Remotely Operated Vehicles equipped with HD cameras are used by the scientist to capture the underwater footages efficiently and accurately. The abundance of the Norway Lobster Nephrops norvegicus stock in the Gulf of Cadiz is assessed based on the identification and counting of the burrows where they live, using underwater videos. The Instituto Espa˜ nol de Oceanograf´ıa (IEO) conducts an annual standard underwater television survey (UWTV) to generate burrow density estimates of Nephrops within a defined 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 experts. This is quite hectic and time consuming job. Computer Vision and Deep learning plays a vital role now a days in detection and classification of objects. The proposed system introduces a deep learning based automated way to identify and classify the Nephrops burrows. The proposed work is using current state of the art Faster RCNN models Inception v2 and MobileNet v2 for objects detection and classification. Tensorflow is used to evaluate the Inception and MobileNet performance with different numbers of training images. The average mean precision of Inception is more than 75% as compared to MobileNet which is 64%. The results show the comparison of Inception and MobileNet detections, as well as the calculation of True Positive and False Positive detections along with undetected burrows.Universidad de Málaga, IEEE, Sir SYED University Karachi-Pakistán, Mehran University Jamshoro-Pakistán, Riphah International Universit

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 toMobileNet, which achieves the best mAP of 75.12%.Versión del edito

Calibración de los parámetros del modelo de directividad de los transductores de la ecosonda Simrad EK60

The scientific echo sounder Simrad EK60 could be used for quantification of fisheries resources in marine science research campaigns. They use different frequency channels (from 18 to 200 KHz), emitted by underwater sound transducers with circular aperture geometries. Calibration is essential to obtain reliable values of Ts (Target Strength) and Sv (Volume Backscattering Coefficient) parameters, which are used to estimate fisheries stocks using echo volume integration. In this paper, manufacturer’s calibration method is presented, together a new proposal for the acquisition and post-processing of calibration data to obtain more accurate results.La ecosonda científica Simrad EK60 se utiliza para cuantificar los recursos pesqueros en campañas de investigación oceanográfica, utilizando diferentes canales de frecuencias (de 18 a 200KHz) mediante transductores acústicos de apertura con geometría circular. Su calibración resulta esencial para obtener los valores de Ts (‘target strength’) y Sv (‘Volume backscattering coefficient’), necesarios para estimar los tamaños de los stocks de las pesquerías mediante integración de los ecos. En este trabajo se presenta el método de calibración propuesto por el fabricante, así como una nueva propuesta de obtención y postproceso de datos que permite obtener calibraciones fiable

Digital image tool to enhance otolith microstructure for estimating age in days in juvenile and adult fish

Age estimation based on fish otolith microstructure analysis is a very repetitive and time-consuming task. The lack of appropriate image analysis software, capable of both overlaying a number of images automatically and recording a high number of daily increments, has been a significant limitation in counting and measuring daily growth increments in large otoliths from juvenile and adult fish individuals. This paper presents a new software to assist marine biologists with faster, more efficient, and more reliable microstructure readings of fish otoliths. Open source code is preferred so that software packages can be updated with new image processing algorithms developed by the scientific community. The approach consists of three steps: 1) a single grayscale digital image combining images of different parts of the same fish otolith is obtained using the blind image registration technique fast normalized cross correlation; 2) the growth rings of the image are enhanced for age estimation purposes, using the adaptive histogram equalization technique; and 3) a semiautomatic interactive tool draws a simple polygonal chain along which the microstructures are easily identifiable and the points of interest can be marked, whose data will be saved automatically. This new tool opens up the opportunity of aging juvenile and adult fish individuals at regular intervals by counting growth rings in the otolith microstructure and facilitates working with other calcified pieces of marine species that exhibit a daily ring pattern, such as cephalopod beaks and mollusk shells