Uses of underwater videos to assess diversity and abundance of fish are being
rapidly adopted by marine biologists. Manual processing of videos for
quantification by human analysts is time and labour intensive...Automatic
processing of videos can be employed to achieve the objectives in a cost and
time-efficient way. The aim is to build an accurate and reliable fish detection
and recognition system, which is important for an autonomous robotic platform. However, there are many challenges involved in this task (e.g. complex
background, deformation, low resolution and light propagation). Recent
advancement in the deep neural network has led to the development of object
detection and recognition in real time scenarios. An end-to-end deep
learning-based architecture is introduced which outperformed the state of the
art methods and first of its kind on fish assessment task. A Region Proposal
Network (RPN) introduced by an object detector termed as Faster R-CNN was
combined with three classification networks for detection and recognition of
fish species obtained from Remote Underwater Video Stations (RUVS). An accuracy
of 82.4% (mAP) obtained from the experiments are much higher than previously
proposed methods.(read more)