Stall Number Detection of Cow Teats Key Frames
This work addresses a domain-specific problem in livestock management by providing an incremental improvement in automated stall number detection.
The paper tackled the problem of detecting stall numbers from cow teat video frames by creating a new dataset and fine-tuning a ResNet34 model, achieving 92% accuracy in recognition and 40.1% IoU in position prediction.
In this paper, we present a small cow stall number dataset named CowStallNumbers, which is extracted from cow teat videos with the goal of advancing cow stall number detection. This dataset contains 1042 training images and 261 test images with the stall number ranging from 0 to 60. In addition, we fine-tuned a ResNet34 model and augmented the dataset with the random crop, center crop, and random rotation. The experimental result achieves a 92% accuracy in stall number recognition and a 40.1% IoU score in stall number position prediction.