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%.
Highlights
The earth’s ecosystem is mainly composed of oceans, as it produces 50% of the oxygen and 97% of the water
5.1.1 Performance We evaluate the performance in terms of mean Average Precision (mAP), which is a prevalent metric in measuring object detectors algorithms’ accuracy like Faster R-Convolution Neural Network (CNN), SSD, etc
We computed the mAP with the dataset of Nephrops from FU22 and FU30 stations over 100k iterations
Summary
The earth’s ecosystem is mainly composed of oceans, as it produces 50% of the oxygen and 97% of the water It is a significant source of our daily food as it provides 15% of proteins in the form of marine animals. In recent years with the advancement of technology, scientists use underwater Remotely Operated Vehicles (ROVs), Autonomous Underwater Vehicles (AUVs), sledge and drop frame structures equipped with highdefinition cameras to record the videos and images of marine species. These vehicles can capture high-definition images and videos. The two main factors which make it difficult are the free natural environment and variations of the visual content, which may arise from variable illumination, scales, views, and non-rigid deformations [2]
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