Fish species must be identified for stock assessments, ecosystem monitoring, production management, and the conservation of endangered species. Implementing algorithms for fish species detection in underwater settings like the Gulf of Mexico poses a formidable challenge. Active learning, a method that efficiently identifies informative samples for annotation while staying within a budget, has demonstrated its effectiveness in the context of object detection in recent times. In this study, we present an active detection model designed for fish species recognition in underwater environments. This model can be employed as an object detection system to effectively lower the expense associated with manual annotation. It uses epistemic uncertainty with Evidential Deep Learning (EDL) and proposes a novel module denoted as Model Evidence Head (MEH) for fish species detection in underwater environments. It employs Hierarchical Uncertainty Aggregation (HUA) to obtain the informativeness of an image. We conducted experiments using a fine-grained and extensive dataset of reef fish collected from the Gulf of Mexico, specifically the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21). The experimental results demonstrate that an active detection framework achieves better detection performance on the SEAMAPD21 dataset demonstrating a favorable balance between performance and data efficiency for underwater fish species recognition.
Accurate recognition of multiple fish species is essential in marine ecology and fisheries. Precisely classifying and tracking these species enriches our comprehension of their movement patterns and empowers us to create precise maps of species-specific territories. Such profound insights are pivotal in conserving endangered species, promoting sustainable fishing practices, and preserving marine ecosystems’ overall health and equilibrium. To partially address these needs, we present a proposed model that combines YOLOv8 for object detection with ByteTrack for tracking. YOLOv8’s oriented bounding boxes help to improve object detection across angles, while ByteTrack’s robustness in various scenarios makes it ideal for real-time tracking. Experimental results using the SEAMAPD21 dataset show the model’s effectiveness, with YOLOv8n being the lightweight yet modestly accurate option, suitable for constrained environments. The study also identifies challenges in fish tracking, such as lighting variations and fish appearance changes, and proposes solutions for future research. Overall, the proposed model shows promising fish tracking and counting results, which is essential for monitoring marine life.
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