Exploring the potential to use low cost imaging and an open source convolutional neural network detector to support stock assessment of the king scallop (Pecten maximus)

Abstract

King Scallop (Pecten maximus) is the third most valuable species landed by UK fishing vessels. This research assesses the potential to use a Convolutional Neural Network (CNN) detector to identify P. maximus in images of the seabed, recorded using low cost camera technology. A ground truth annotated dataset of images of P. maximus captured in situ was collated. Automatic scallop detectors built into the Video and Image Analytics for Marine Environments (VIAME) toolkit were evaluated on the ground truth dataset. The best performing CNN (NetHarn_1_class) was then trained on the annotated training dataset (90% of the ground truth set) to produce a new detector specifically for P. maximus. The new detector was evaluated on a subset of 208 images (10% of the ground truth set) with the following results: Precision 0.97, Recall 0.95, F1 Score of 0.96, mAP 0.91, with a confidence threshold of 0.5. These results strongly suggest that application of machine learning and optimisation of the low cost imaging approach is merited with a view to expanding stock assessment and scientific survey methods using this non-destructive and more cost-effective approach.