Technology-driven precision livestock farming (PLF) empowers practitioners to
monitor and analyze animal growth and health conditions for improved
productivity and welfare. Computer vision (CV) is indispensable in PLF by using
cameras and computer algorithms to supplement or supersede manual efforts for
livestock data acquisition. Data availability is crucial for developing
innovative monitoring and analysis systems through artificial
intelligence-based techniques. However, data curation processes are tedious,
time-consuming, and resource intensive. This study presents the first
systematic survey of publicly available livestock CV datasets
(this https URL).
Among 58 public datasets identified and analyzed, encompassing different
species of livestock, almost half of them are for cattle, followed by swine,
poultry, and other animals. Individual animal detection and color imaging are
the dominant application and imaging modality for livestock. The
characteristics and baseline applications of the datasets are discussed,
emphasizing the implications for animal welfare advocates. Challenges and
opportunities are also discussed to inspire further efforts in developing
livestock CV datasets. This study highlights that the limited quantity of
high-quality annotated datasets collected from diverse environments, animals,
and applications, the absence of contextual metadata, are a real bottleneck in
PLF.

这项研究首次系统调查了公开可获得的牲畜计算机视觉数据集，并讨论了数据集的特征、应用及其对动物福利的影响。同时，还探讨了开发牲畜计算机视觉数据集所面临的挑战和机遇，指出数据质量和标注数量的限制是牲畜精准畜牧中的一个瓶颈。

面向精准畜牧农业的公共计算机视觉数据集：系统调查

Public Computer Vision Datasets for Precision Livestock Farming: A  Systematic Survey

This paper studies a reconstruction-based approach for weakly-supervised
animal detection from aerial images in marine environments. Such an approach
leverages an anomaly detection framework that computes metrics directly on the
input space, enhancing interpretability and anomaly localization compared to
feature embedding methods. Building upon the success of Vector-Quantized
Variational Autoencoders in anomaly detection on computer vision datasets, we
adapt them to the marine animal detection domain and address the challenge of
handling noisy data. To evaluate our approach, we compare it with existing
methods in the context of marine animal detection from aerial image data.
Experiments conducted on two dedicated datasets demonstrate the superior
performance of the proposed method over recent studies in the literature. Our
framework offers improved interpretability and localization of anomalies,
providing valuable insights for monitoring marine ecosystems and mitigating the
impact of human activities on marine animals.

本文讨论了一种从海洋环境中的航空图像中进行弱监督动物检测的重建方法，该方法利用异常检测框架直接计算输入空间的度量，与特征嵌入方法相比具有更好的可解释性和异常定位能力，同时在处理嘈杂数据方面具有优势，提供了有价值的洞察力，推动了监测海洋生态系统并减缓人类活动对海洋动物的影响的发展。

利用向量量化变分自编码器从遥感影像中进行弱监督海洋动物检测

Weakly supervised marine animal detection from remote sensing images  using vector-quantized variational autoencoder

Datasets that allow the training of common objects or human detectors are
widely available. These come in the form of labelled real-world images and
require either a significant amount of human effort, with a high probability of
errors such as missing labels, or very constrained scenarios, e.g. VICON
systems. Likewise, uncommon scenarios, like aerial views, animals, like wild
zebras, or difficult-to-obtain information as human shapes, are hardly
available. To overcome this, usage of synthetic data generation with realistic
rendering technologies has recently gained traction and advanced tasks like
target tracking and human pose estimation. However, subjects such as wild
animals are still usually not well represented in such datasets. In this work,
we first show that a pre-trained YOLO detector can not identify zebras in real
images recorded from aerial viewpoints. To solve this, we present an approach
for training an animal detector using only synthetic data. We start by
generating a novel synthetic zebra dataset using GRADE, a state-of-the-art
framework for data generation. The dataset includes RGB, depth, skeletal joint
locations, pose, shape and instance segmentations for each subject. We use this
to train a YOLO detector from scratch. Through extensive evaluations of our
model with real-world data from i) limited datasets available on the internet
and ii) a new one collected and manually labelled by us, we show that we can
detect zebras by using only synthetic data during training. The code, results,
trained models, and both the generated and training data are provided as
open-source at this https URL

使用合成数据和 YOLO 目标检测器，实现了从航拍图像中检测野生动物斑马（无需人工标注）的神经网络训练，并开放了代码与数据集。