Lightweight marine biodetection model based on improved YOLOv10

In IoT-enabled marine biology, real-time monitoring of marine organisms faces challenges due to blurred images and complex underwater backgrounds, which hinder feature extraction and lead to missed detections. Addressing these issues, the lightweight YOLOv10-AD model introduces AKVanillaNet, a novel backbone optimized for the distinct shapes of marine organisms, improving detection accuracy while minimizing parameters and computational cost. Additionally, the model incorporates the DysnakeConv module within the C2f structure to enhance feature extraction, along with the Powerful-IOU (PIOU) loss function for better data fitting. Testing on URPC dataset shows that YOLOv10-AD achieves an mAP of 85.7%, with a…