Open Access

ARTICLE

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

Atif Naseer^1,*, Enrique Nava Baro¹, Sultan Daud Khan², Yolanda Vila³, Jennifer Doyle⁴

1 ETSI Telecomunicación, Universidad de Málaga, Málaga, 29071, Spain
2 Department of Computer Science, National University of Technology, Islamabad, 44000, Pakistan
3 Instituto Español de Oceanografía, Centro Oceanográfico de Cádiz, Cádiz, 39004, Spain
4 Marine Institute Rinville, Oranmore, Ireland

* Corresponding Author: Atif Naseer. Email:

Computers, Materials & Continua 2022, 70(3), 5321-5344. https://doi.org/10.32604/cmc.2022.020886

Received 12 June 2021; Accepted 12 August 2021; Issue published 11 October 2021

Abstract

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) and Marine Institute Ireland (MI-Ireland) 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 the MobileNet model. 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%.

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Keywords

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