Detection and Tracking of Nile Tilapia Using DeepLabCut with ResNet50 and MobileNetV2

Nile tilapia (Oreochromis niloticus) is vital for aquaculture due to its rapid growth and high productivity. This study tackles detection and tracking concerns using machine vision techniques, developing an automated system with DeepLabCut™ and training classifiers with ResNet50 and MobileNet V2-1.0. MobileNet V2 showed balanced performance, with lower training (634.5 pixels) and test errors (723.7 pixels), and a higher mean likelihood (0.60) for the body region. ResNet50, despite efficient learning, suffered from overfitting, with training and test errors of 1121.2 and 1083.3 pixels, and low mean likelihood (0.1) for all body parts. These results indicate MobileNet V2 is better for reliable, resource-efficient predictions, while ResNet50 could improve with better data quality and annotations. Future research should focus on enhancing data quality, extended training, and advanced regularization techniques.