Akademik Veri Yönetim Sistemi
Signal, Image and Video Processing, cilt.18, sa.10, ss.6683-6694, 2024 (SCI-Expanded)
This research emphasises the importance of sheep identification and tracking in precision livestock farming and investigates the use of deep learning techniques for this purpose. Since traditional identification methods are time consuming and limiting, it is hypothesised that deep learning based models can make this process more efficient. However, although deep learning-based methods have achieved remarkable results in the field of animal recognition, some problems can be encountered that limit their practical application. Generally, these networks are tested on similar images taken from the dataset on which they are trained. Although the test performance of these models is high, they may perform poorly on images with different features. For this reason, in the present study on the YOLOv5 model, a number of effective preprocesses are included for the model’s ability to identify and track sheep from sheep images with different traits from the training data. In addition, some adaptive adjustments were made to the YOLOv5 model to increase its effectiveness in practical applications. According to the experimental results of this study, in which videos of 20 Norduz sheep in the scale and arena tests were used, the YOLOv5l model trained on the scale test reached a mAP value of 0.99. Although the model performed the task of identifying and tracking the sheep in the scale test, it was observed that it could not perform the task of identifying and tracking the sheep in the arena test. Therefore, YOLOV5l (Model II), which was retrained on the scale images segmented from the background, gained the ability to identify and track the sheep in the arena test with some various pre-tuning. The findings of the study indicate the potential of deep learning-based models to improve the effectiveness of animal identification and tracking procedures in precision livestock farming. At the same time, the developmental stages outlined in this study provide a reference for the identification and tracking of sheep or alternative livestock in real-life situations.