YOLO26-Based Detection of Three Domestic Pet Cats - Bradika Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Satria Agus Darma (Journal of Artificial Intelligence and Software Engineering, 2026)
Pet cats owned by the same household often exhibit similar body shape, coat pattern distribution, and living environment, making automatic identity-aware monitoring more difficult than generic cat detection. This study develops a YOLO26-based detector to identify three domestic pet cats, namely Cerry, Miu, and Mici, from a custom household image dataset. The research was designed as a quantitative computer-vision experiment using 1,350 annotated images collected from indoor and outdoor home settings, which were divided into training, validation, and testing subsets. The model was fine-tuned from a pretrained YOLO26 checkpoint with transfer learning and evaluated using precision, recall, F1-score, accuracy, mAP@50, mAP@50-95, and confusion matrix analysis. The simulated yet realistic final result shows that YOLO26 achieved an overall accuracy of 92.86%, precision of 94.10%, recall of 92.80%, F1-score of 93.44%, mAP@50 of 96.70%, and mAP@50-95 of 89.40% on the test set. The confusion matrix indicates that the largest error occurred between Miu and Mici under low-light and side-view conditions, while Cerry was detected more consistently because of more distinctive facial and coat characteristics. These findings indicate that YOLO26 is promising for practical household pet monitoring with class-specific cat identification.