A model generalization study in localizing indoor cows with cow localization (colo) dataset
| dc.contributor.author | Das, Mautushi | en |
| dc.contributor.committeechair | Chen, Chun-Peng | en |
| dc.contributor.committeemember | Ferreira, Gonzalo | en |
| dc.contributor.committeemember | White, Robin | en |
| dc.contributor.committeemember | Petersson-Wolfe, Christina Sonja | en |
| dc.contributor.department | Animal and Poultry Sciences | en |
| dc.date.accessioned | 2024-07-11T08:00:56Z | en |
| dc.date.available | 2024-07-11T08:00:56Z | en |
| dc.date.issued | 2024-07-10 | en |
| dc.description.abstract | Precision livestock farming increasingly relies on advanced object localization techniques to monitor livestock health and optimize resource management. In recent years, computer vision-based localization methods have been widely used for animal localization. However, certain challenges still make the task difficult, such as the scarcity of data for model fine-tuning and the inability to generalize models effectively. To address these challenges, we introduces COLO (COw LOcalization), a publicly available dataset comprising localization data for Jersey and Holstein cows under various lighting conditions and camera angles. We evaluate the performance and generalization capabilities of YOLOv8 and YOLOv9 model variants using this dataset. Our analysis assesses model robustness across different lighting and viewpoint configurations and explores the trade-off between model complexity, defined by the number of learnable parameters, and performance. Our findings indicate that camera viewpoint angle is the most critical factor for model training, surpassing the influence of lighting conditions. Higher model complexity does not necessarily guarantee better results; rather, performance is contingent on specific data and task requirements. For our dataset, medium complexity models generally outperformed both simpler and more complex models. Additionally, we evaluate the performance of fine-tuned models across various pre-trained weight initialization. The results demonstrate that as the amount of training samples increases, the advantage of using weight initialization diminishes. This suggests that for large datasets, it may not be necessary to invest extra effort in fine-tuning models with custom weight initialization. In summary, our study provides comprehensive insights for animal and dairy scientists to choose the optimal model for cow localization performance, considering factors such as lighting, camera angles, model parameters, dataset size, and different weight initialization criteria. These findings contribute to the field of precision livestock farming by enhancing the accuracy and efficiency of cow localization technology. The COLO dataset, introduced in this study, serves as a valuable resource for the research community, enabling further advancements in object detection models for precision livestock farming. | en |
| dc.description.abstractgeneral | Cow localization is important for many reasons. Farmers want to monitor cows to understand their behavior, count cows in a scene, and track their activities such as eating and grazing. Popular technologies like GPS or other tracking devices need to be worn by cows in the form of collars, ear tags etc. This requires manually putting the device on each cow, which is labor-intensive and costly since each cow needs its own device. In contrast, computer vision-based methods need only one camera to effectively track and monitor cows. We can use deep learning models and a camera to detect cows in a scene. This method is cost-effective and does not require strict maintenance. However, this approach still has challenges. Deep learning models need a large amount of data to train, and there is a lack of annotated data in our community. Data collection and preparation for model training require human labor and technical skills. Additionally, to make the model robust, it needs to be adjusted effectively, a process called model generalization. Our work addresses these challenges with two main contributions. First, we introduce a new dataset called COLO (COw LOcalization). This dataset consists of over 1,000 annotated images of Holstein and Jersey cows. Anyone can use this data to train their models. Second, we demonstrate how to generalize models. This model generalization method is not only applicable for cow localization but can also be adapted for other purposes whenever deep learning models are used. In numbers, we found that the YOLOv8m model is the optimal model for cow localization using our dataset. Additionally, we discovered that camera angle is a crucial factor for model generalization. This means that where we place the camera on the farm is important for getting accurate predictions. We found that top angles (placing the camera above) provide better accuracy. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:41139 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/120639 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Object detection | en |
| dc.subject | Cows | en |
| dc.subject | Model generalization | en |
| dc.subject | Model selection | en |
| dc.title | A model generalization study in localizing indoor cows with cow localization (colo) dataset | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Animal and Poultry Sciences | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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