Abstract: Marine experts are facing lot of challenges in habitat monitoring of marine species. One of the biggest challenges is the underwater environment and species movement. The other challenge is the data collection of marine species. People used the camera sensors and satellite data in the past for data collection but in this era the scientists are using underwater Autonomous Underwater Vehicles (AUVs), the Remotely Operated Vehicles (ROVs), and certain sledges with high-definition still and video cameras to record the underwater footages. The ocean is composed of thousands of species which make the environment more challenging to monitor any specific specie. This work will focus on specie named Norway lobster (Nephrops norvegicus). The Nephrops norvegicus is one of the commercial specie in the Europe and generates millions of dollars yearly. This specie lives under the seabed and leaves behind the burrow structure on the sea ground. The Nephrops spend most of their time under the seabed. The scientists are currently monitoring the habitat of Nephrops norvegicus by underwater television (UWTV) surveys that is collected yearly on many European grounds. The collected data is reviewed manually by the experts who count the burrows on the sheet. This work focuses on the automatic detection of Nephrops burrows from underwater videos using the deep learning techniques. This work trained the Faster R-CNN models Inceptionv2, MobileNetv2, ResNet50, and ResNet101. Instead of training the models from scratch we used the transfer learning technique to fine tune these networks. The data is obtained from the Gulf of Cadiz (FU30) station. Twenty-eight different set of experiments are performed. The models are evaluated quantitatively using the mean Average Precision (mAP), precision and recall curves. Also, the models are qualitatively analyzed by visually presenting the output. The results prove that deep learning techniques are very helpful for marine scientists to assess the Nephrops norvegicus abundance.

Atif Naseer, “Advanced Detections of Norway Lobster (Nephrops Norvegicus) Burrows using Deep Learning Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 14(3), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140357

@article{Naseer2023,
title = {Advanced Detections of Norway Lobster (Nephrops Norvegicus) Burrows using Deep Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140357},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140357},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Atif Naseer}
}