CattleDeSegNet: a joint approach to cattle denoising and interpretable segmentation

Sivaji Retta; Ramarajulu Srinivasan; Shawn Tan

doi:10.1117/12.3023338

3 April 2024 CattleDeSegNet: a joint approach to cattle denoising and interpretable segmentation

Sivaji Retta, Ramarajulu Srinivasan, Shawn Tan

Proceedings Volume 13072, Sixteenth International Conference on Machine Vision (ICMV 2023); 130720A (2024) https://doi.org/10.1117/12.3023338
Event: Sixteenth International Conference on Machine Vision (ICMV 2023), 2023, Yerevan, Armenia

Abstract

In modern agriculture, livestock monitoring plays a vital role in ensuring animal health, welfare, and production efficiency. Leveraging computer vision and deep learning, this paper presents an innovative framework aimed at enhancing livestock monitoring. Specifically, we address two crucial challenges: denoising and segmentation of cattle in livestock images. The denoising task is fundamental in preprocessing noisy images affected by adverse environmental conditions and equipment limitations. To tackle this, we introduce an encoder-decoder model that effectively denoises cattle images while preserving critical anatomical details. Our framework incorporates a segmentation module inspired by the U-Net architecture. Notably, both denoising and segmentation tasks share a common encoder, optimizing computational efficiency. The segmentation model employs hybrid loss functions and leverages the Grad-CAM technique to provide interpretable insights into the decision-making process. Our approach stands as one of the pioneering joint solutions for cattle denoising and segmentation, particularly focusing on top-view cattle images.

Citation Download Citation

Sivaji Retta, Ramarajulu Srinivasan, and Shawn Tan "CattleDeSegNet: a joint approach to cattle denoising and interpretable segmentation", Proc. SPIE 13072, Sixteenth International Conference on Machine Vision (ICMV 2023), 130720A (3 April 2024); https://doi.org/10.1117/12.3023338

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