Stall Number Detection of Cow Teats Key Frames
Youshan Zhang

TL;DR
This paper introduces the CowStallNumbers dataset for cow stall number detection, and demonstrates that fine-tuning ResNet34 with data augmentation achieves high accuracy in recognizing stall numbers.
Contribution
The paper provides a new dataset for cow stall number detection and evaluates a deep learning approach with data augmentation for improved accuracy.
Findings
92% accuracy in stall number recognition
40.1% IoU score in stall number position prediction
Dataset contains 1303 images with stall numbers from 0 to 60
Abstract
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.
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Taxonomy
TopicsFood Supply Chain Traceability · Poxvirus research and outbreaks · Speech and Audio Processing
MethodsTest
