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