Detection and classification of protected species bycatch in Swedish small-scale fisheries
Typ
Examensarbete för masterexamen
Program
Publicerad
2022
Författare
Basheer, Feroz
Abdullah, Muhammad
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Bycatches are an adverse side effect of fishing. Though rare, their occurrences
have a serious impact on PETS(protected, endangered, and threatened species). In
this paper, the feasibility of machine learning in the video feed analysis process
to aid bycatch research is conducted and it is found that this niche benefits from
applied machine learning. Different object detection algorithms are implemented on
bycatch datasets built from scratch. The object detection models are compared on
metrics such as average precision, mean average precision and recall to pick a model
that is best suited for the bycatch dataset. It is also discussed how the machine
learning model could benefit from diversifying the dataset while addressing key
concerns of sharing data between different stakeholders. This concern is addressed
by the adaptation of federated learning. A hierarchical federated machine learning
framework (FEDn from Scaleout) is implemented to train YOLOv5s with Swedish
and Danish clients(local models). The results obtained show that although the
clients learn from each other, the rate of convergence is far slower than the locally
trained models therefore requires fine-tuning and needs rethinking of global weights
aggregation that determines how the clients learn from each other. Finally, it is
concluded that with a good quality dataset the object detection model can be used
as an aid for researchers, potentially helping them identify bycatch even when a
human fails to identify them.
Beskrivning
Ämne/nyckelord
Federated Learning, Object Detection, Bycatch, YOLO, PETS