Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

Xu, Beibei; Wang, Wensheng; Guo, Lei; Chen, Guipeng; Wang, Yaowu; Zhang, Wenju; Li, Yongfeng

doi:10.3390/agriculture11111062

Cited by 29 publications

(9 citation statements)

References 52 publications

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“…In previous researches, the mouth's AP of cattle was 87.8% [19], our mouth's AP was 93.6%, there is a little improvement, and the head's AP of cattle was 99.8% [31], our head's AP was 98.7%, which was a little lower than it, but according to the object detection result, the multi-object cattle heads and mouths could be detected well. And all tracking videos were converted into images, we checked them manually and couldn't find the missing detected objects, so we thought the YOLO and KCF could be used in tracking heads of multi-object cattle.…”

Section: Training Resultsmentioning

confidence: 78%

“…The aforementioned object-tracking methods mainly tracked the whole body of animal, they are not suitable for head-tracking. Xu et al used RetinaNet-based detection model for the detection of multiview cattle faces [31]. The objective of this study was to find a new way to automatically monitor and analyse cattle rumination using visual rumination monitoring programs, with no physical contact.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic identification and analysis of multi-object cattle rumination based on computer vision

Wang¹,

Chen²,

Li³

et al. 2023

J Anim Sci Technol

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Section: Training Resultsmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Automatic identification and analysis of multi-object cattle rumination based on computer vision

Wang¹,

Chen²,

Li³

et al. 2023

J Anim Sci Technol

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“…Xu et al [12] used RetinaNet with ResNet-50 for cattle face detection. They found an average precision score of 99.8%.…”

Section: B Animal Face Detectionmentioning

confidence: 99%

Biometric-Based Security System for Smart Riding Clubs

et al. 2022

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Horses, workers or riders need safety in a farm or a riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and recognize the identity of each worker or rider and horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse as well as the human gait biometrics due to their simplicity and intuitiveness in an uncontrolled environment. This work suggests a Siamese network based on DenseNet features for human gait recognition, a Sparse Neural Network (SNN) based on sparse features for horse face detection and a horse face recognition method based on Gabor features, LDA and SVM. Because of the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. The proposed systems achieved an average accuracy of gait recognition equal to 95% in the 0°view, 100% in the 90°view and 98.90% in the 180°view on Casia-B dataset, an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.INDEX TERMS Horse face detection, human gait recognition, neural network, siamese network, smart riding club, THDD.

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“…There are only a few works in the literature about animal face detection. For instance, a RetinaNet [26] object detection model was used to perform multi-view cattle face detection in housing farms [27]. The authors collected and manually annotated 3,000 images of 85 cattle to train the model.…”

Section: Related Workmentioning

confidence: 99%

AnyFace: A Data-Centric Approach For Input-Agnostic Face Detection

Kuzdeuov¹,

Koishigarina²,

Varol³

2022

Preprint

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<p>Face detection is a mandatory step in many computer vision applications, such as face recognition, emotion recognition, age detection, virtual makeup, and vital sign monitoring. Thanks to advancements in deep learning and the introduction of annotated large-scale datasets, numerous applications have been developed for human faces. Recently, other domains, such as animals and cartoon characters, have started gaining attention but still lag far behind human faces. The biggest challenge is the limited number of annotated face datasets in these domains. The manual labeling of large-scale datasets is tedious and requires substantial human labor. In this regard, we present an input-agnostic face detector to ease the annotation of various face datasets. We propose a simple but effective data-centric approach instead of building a specific neural network architecture. Specifically, we trained a face detection model, YOLO5Face, on human, animal, and cartoon face datasets. The experiments show that the model can achieve accurate results in all domains. In addition, the model achieved decent results for animals and cartoon characters different from the ones in the training set. This implies that the model can extract agnostic facial features. We have made the source code and pre-trained models publicly available at https://github.com/IS2AI/AnyFace to stimulate research in these fields.</p>

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Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

Cited by 29 publications

References 52 publications

Automatic identification and analysis of multi-object cattle rumination based on computer vision

Automatic identification and analysis of multi-object cattle rumination based on computer vision

Biometric-Based Security System for Smart Riding Clubs

AnyFace: A Data-Centric Approach For Input-Agnostic Face Detection

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