Cattle Identification Using Muzzle Images and Deep Learning Techniques

• URL: http://arxiv.org/abs/2311.08148v1
• Date: Tue, 14 Nov 2023 13:25:41 GMT
• Title: Cattle Identification Using Muzzle Images and Deep Learning Techniques
• Authors: G. N. Kimani, P. Oluwadara, P. Fashingabo, M. Busogi, E. Luhanga, K. Sowon, L. Chacha ((1) CyLab-Africa / Upanzi Network, (2) Carnegie Mellon University Africa and (3) Carnegie Mellon University Pittsburgh)
• Abstract summary: This project explores cattle identification using 4923 muzzle images collected from 268 beef cattle. From the experiments run, a maximum accuracy of 99.5% is achieved while using the wide ResNet50 model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional animal identification methods such as ear-tagging, ear notching, and branding have been effective but pose risks to the animal and have scalability issues. Electrical methods offer better tracking and monitoring but require specialized equipment and are susceptible to attacks. Biometric identification using time-immutable dermatoglyphic features such as muzzle prints and iris patterns is a promising solution. This project explores cattle identification using 4923 muzzle images collected from 268 beef cattle. Two deep learning classification models are implemented - wide ResNet50 and VGG16\_BN and image compression is done to lower the image quality and adapt the models to work for the African context. From the experiments run, a maximum accuracy of 99.5\% is achieved while using the wide ResNet50 model with a compression retaining 25\% of the original image. From the study, it is noted that the time required by the models to train and converge as well as recognition time are dependent on the machine used to run the model.

Related papers

• Frequency-Guided Masking for Enhanced Vision Self-Supervised Learning [49.275450836604726]
  We present a novel frequency-based Self-Supervised Learning (SSL) approach that significantly enhances its efficacy for pre-training. We employ a two-branch framework empowered by knowledge distillation, enabling the model to take both the filtered and original images as input.
  arXiv  Detail & Related papers  (2024-09-16T15:10:07Z)
• Muzzle-Based Cattle Identification System Using Artificial Intelligence (AI) [0.0]
  The uniqueness of cattle muzzles has been scientifically established, which resembles human fingerprints. This is the premise that prompted us to develop a cattle identification system that extracts the uniqueness of cattle muzzles. Our system performs with an accuracy of $96.489%$, $F_1$ score of $97.334%$, and a true positive rate (tpr) of $87.993%$ at a remarkably low false positive rate (fpr) of $0.098%$.
  arXiv  Detail & Related papers  (2024-07-08T16:36:55Z)
• How to Trace Latent Generative Model Generated Images without Artificial Watermark? [88.04880564539836]
  Concerns have arisen regarding potential misuse related to images generated by latent generative models. We propose a latent inversion based method called LatentTracer to trace the generated images of the inspected model. Our experiments show that our method can distinguish the images generated by the inspected model and other images with a high accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-05-22T05:33:47Z)
• Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images [57.96659470133514]
  Motion-activated camera traps constitute an efficient tool for tracking and monitoring wildlife populations across the globe. Supervised learning techniques have been successfully deployed to analyze such imagery, however training such techniques requires annotations from experts. Reducing the reliance on costly labelled data has immense potential in developing large-scale wildlife tracking solutions with markedly less human labor.
  arXiv  Detail & Related papers  (2023-11-02T08:32:00Z)
• Detecting Generated Images by Real Images Only [64.12501227493765]
  Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace.
  arXiv  Detail & Related papers  (2023-11-02T03:09:37Z)
• Improving Masked Autoencoders by Learning Where to Mask [65.89510231743692]
  Masked image modeling is a promising self-supervised learning method for visual data. We present AutoMAE, a framework that uses Gumbel-Softmax to interlink an adversarially-trained mask generator and a mask-guided image modeling process. In our experiments, AutoMAE is shown to provide effective pretraining models on standard self-supervised benchmarks and downstream tasks.
  arXiv  Detail & Related papers  (2023-03-12T05:28:55Z)
• Rare Wildlife Recognition with Self-Supervised Representation Learning [0.0]
  We present a methodology to reduce the amount of required training data by resorting to self-supervised pretraining. We show that a combination of MoCo, CLD, and geometric augmentations outperforms conventional models pretrained on ImageNet by a large margin.
  arXiv  Detail & Related papers  (2022-10-29T17:57:38Z)
• Bag of Tricks for Long-Tail Visual Recognition of Animal Species in Camera Trap Images [2.294014185517203]
  We evaluate recently proposed techniques to address the long-tail visual recognition of animal species in camera trap images. In general, the square-root sampling was the method that most improved the performance for minority classes by around 10%. The proposed approach achieved the best trade-off between the performance of the tail class and the cost of the head classes' accuracy.
  arXiv  Detail & Related papers  (2022-06-24T18:30:26Z)
• How many images do I need? Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring [0.0]
  We explore in depth the issues of deep learning model performance for progressively increasing per class (species) sample sizes. We provide ecologists with an approximation formula to estimate how many images per animal species they need for certain accuracy level a priori.
  arXiv  Detail & Related papers  (2020-10-16T06:28:35Z)
• Automatic Detection and Recognition of Individuals in Patterned Species [4.163860911052052]
  We develop a framework for automatic detection and recognition of individuals in different patterned species. We use the recently proposed Faster-RCNN object detection framework to efficiently detect animals in images. We evaluate our recognition system on zebra and jaguar images to show generalization to other patterned species.
  arXiv  Detail & Related papers  (2020-05-06T15:29:21Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.