Evaluating model generalization for cow detection in free-stall barn settings: Insights from the COw LOcalization (COLO) dataset - Mautushi Das; Gonzalo Ferreira; C.P. James Chen (Smart Agricultural Technology)
Precision livestock farming (PLF) increasingly relies on advanced object localization techniques to monitor livestock health and optimize resource management. This study investigates the generalization capabilities of object detection models for cow detection in indoor free-stall barn settings, focusing on varying training data characteristics such as view angles and lighting, and model complexities. Leveraging the newly released public dataset, COws LOcalization (COLO) dataset, we explore three key hypotheses: (1) Model generalization is equally influenced by changes in lighting conditions and camera angles; (2) Higher model complexity guarantees better generalization performance; (3) Fine-tuning with custom initial weights trained on relevant tasks always brings advantages to detection tasks. Our findings reveal considerable challenges in detecting cows in images taken from side views and underscore the importance of including diverse camera angles in building a detection model. Furthermore, our results emphasize that higher model complexity does not necessarily lead to better performance. The optimal model configuration heavily depends on the specific task and dataset, highlighting the need for careful model selection tailored to the particular application. Lastly, while fine-tuning with transferred weights from related tasks can significantly benefit detection performance, especially when the source and target domains are closely aligned and the available labeled data is limited, this advantage diminishes as domain divergence increases or as more labeled data becomes available. In such cases, initializing with general pre-trained weights is often sufficient and more efficient, eliminating the need for labor-intensive task-specific weight initialization. Future work should focus on adaptive methods and advanced data augmentation to improve generalization and robustness. This study provides practical guidelines for PLF researchers on deploying computer vision models from existing studies, highlights generalization issues, and contributes the COLO dataset containing 1,254 images and 11,818 cow instances for further research.