VC-CAD: novel learning rate decay strategy for efficient and accurate animal behavior recognition

In computer vision, animals' health and physiological conditions can be analyzed by monitoring their behaviors. However, the utilization of accurate animal behavior recognition models frequently involves significant computational complexity. By optimizing the learning rate, one can prevent changes in model complexity. However, when using the SGD (Stochastic Gradient Descent) optimizer with faster training speed, there is a risk of being stuck in a local optimal solution. More concretely, this paper introduces a novel algorithm called Variable-Cycle Cosine Annealing Decay (VC-CAD) that addresses the issue of the model getting stuck in a local optimal solution. Additionally, this paper introduces a novel concept of efficiency value by integrating training cost and detection accuracy. It also formulates the efficiency equation of recognition to evaluate the recognition efficiency of the model. The experimental results demonstrate that the integration of VC-CAD with YOLOv5s and YOLOv5m resulted in an increase in mAP (mean Average Precision) of 1.7% and 1.6% respectively. The performance of YOLOv5s and YOLOv5m was improved by 3.4% and 0.8%, respectively, through a comparative analysis of different models with varying learning rates, thereby validating the performance enhancement achieved by the VC-CAD algorithm within the YOLOv5 framework.