Real-Time Fish Detection in Indonesian Marine Ecosystems Using Lightweight YOLOv10-nano Architecture
This addresses the need for efficient marine monitoring tools for conservationists in data-limited environments, though it's an incremental application of an existing method to new data.
This study tackled automated fish detection in Indonesian marine ecosystems by implementing YOLOv10-nano, achieving high accuracy with mAP50 of 0.966 and mAP50:95 of 0.606 while maintaining real-time performance at 29.29 FPS on CPU.
Indonesia's marine ecosystems, part of the globally recognized Coral Triangle, are among the richest in biodiversity, requiring efficient monitoring tools to support conservation. Traditional fish detection methods are time-consuming and demand expert knowledge, prompting the need for automated solutions. This study explores the implementation of YOLOv10-nano, a state-of-the-art deep learning model, for real-time marine fish detection in Indonesian waters, using test data from Bunaken National Marine Park. YOLOv10's architecture, featuring improvements like the CSPNet backbone, PAN for feature fusion, and Pyramid Spatial Attention Block, enables efficient and accurate object detection even in complex environments. The model was evaluated on the DeepFish and OpenImages V7-Fish datasets. Results show that YOLOv10-nano achieves a high detection accuracy with mAP50 of 0.966 and mAP50:95 of 0.606 while maintaining low computational demand (2.7M parameters, 8.4 GFLOPs). It also delivered an average inference speed of 29.29 FPS on the CPU, making it suitable for real-time deployment. Although OpenImages V7-Fish alone provided lower accuracy, it complemented DeepFish in enhancing model robustness. Overall, this study demonstrates YOLOv10-nano's potential for efficient, scalable marine fish monitoring and conservation applications in data-limited environments.