Using Pruning-Based YOLOv3 Deep Learning Algorithm for Accurate Detection of Sheep Face

Song, Shuang; Liu, Tonghai; Wang, Hai; Hasi, Bagen; Yuan, Chuangchuang; Gao, Fangyu; Shi, Hongxiao

doi:10.3390/ani12111465

Cited by 27 publications

(13 citation statements)

References 41 publications

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“…Deep learning tries to draw conclusions, much like humans analyze data based on sequential logical structures. To achieve this goal, deep learning uses a multilayer structure called a neural network (Neural Network) [14].…”

Section: Deep Learning (Deep Learning)mentioning

confidence: 99%

Design and Development of a Coffee Bean Selector Using The Yolo Algorithm

Putri

Tiara

2022

Int. J. Eng. Scie. and Inform. Technology.

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The enjoyment of brewed coffee is determined by many things, one of which is choosing quality coffee beans and coffee powder. Quality coffee beans are determined by shape and size (full, half full, or damaged). Checking the size and shape of coffee beans manually is subject to external influences such as fatigue, environment, light, etc. With the help of technologyimage processing, these factors can be overcome. Through this paper a tool is designed to detect coffee defects using the Yolo algorithm. The designed system consists of a camera that captures images of coffee beans which are then processed by the Raspberry Pi and the results are displayed on the laptop screen. Detection results using the YOLO algorithm for 10 trials using 100 coffee beans get an average percentage value of 76.54% for the perfect coffee bean category of 43 coffee beans, then the average percentage of imperfect coffee beans is 73.40% with lots of 48 3 coffee beans and coffee beans were not detected with an average percentage value of 1% and coffee beans included in the two categories were 6 coffee beans with an average percentage value of 19.8%. In this study the YOLO algorithm can maintain an accuracy rate of detection success of 75%.

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Section: Deep Learning (Deep Learning)mentioning

confidence: 99%

Design and Development of a Coffee Bean Selector Using The Yolo Algorithm

Putri

Tiara

2022

Int. J. Eng. Scie. and Inform. Technology.

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“…The authors collected and annotated 2,222 images to train the model. Similarly, a YOLOv3 was trained to detect sheep faces [33]. The authors collected and labeled 1,958 sheep face images.…”

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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“…In recent years, scholars have used computer vision technology to recognize livestock faces, and various CNNs have been developed for the task of identification [12][13][14]. Song et al [15] used an improved YOLOv3 model to recognize 20 adult Sunit sheep, and the mAP reached 97.2%. Although the model size of improved YOLOv3 has been reduced from the initial 235 MB to 61 MB to reduce computational costs, the recognition model still has large parameters, which is not conducive to deployment on mobile devices.…”

Section: Introductionmentioning

confidence: 99%

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

Zhang

Xuan

Jing

et al. 2023

Animals

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The accurate identification of sheep is crucial for breeding, behavioral research, food quality tracking, and disease prevention on modern farms. As a result of the time-consuming, expensive, and unreliable problems of traditional sheep-identification methods, relevant studies have built sheep face recognition models to recognize sheep through facial images. However, the existing sheep face recognition models face problems such as high computational costs, large model sizes, and weak practicality. In response to the above issues, this study proposes a lightweight sheep face recognition model named LSR-YOLO. Specifically, the ShuffleNetv2 module and Ghost module were used to replace the feature extraction module in the backbone and neck of YOLOv5s to reduce floating-point operations per second (FLOPs) and parameters. In addition, the coordinated attention (CA) module was introduced into the backbone to suppress non-critical information and improve the feature extraction ability of the recognition model. We collected facial images of 63 small-tailed Han sheep to construct a sheep face dataset and further evaluate the proposed method. Compared to YOLOv5s, the FLOPs and parameters of LSR-YOLO decreased by 25.5% and 33.4%, respectively. LSR-YOLO achieved the best performance on the sheep face dataset, and the mAP@0.5 reached 97.8% when the model size was only 9.5 MB. The experimental results show that LSR-YOLO has significant advantages in recognition accuracy and model size. Finally, we integrated LSR-YOLO into mobile devices and further developed a recognition system to achieve real-time recognition. The results show that LSR-YOLO is an effective method for identifying sheep. The method has high recognition accuracy and fast recognition speed, which gives it a high application value in mobile recognition and welfare breeding.

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Using Pruning-Based YOLOv3 Deep Learning Algorithm for Accurate Detection of Sheep Face

Cited by 27 publications

References 41 publications

Design and Development of a Coffee Bean Selector Using The Yolo Algorithm

Design and Development of a Coffee Bean Selector Using The Yolo Algorithm

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

LSR-YOLO: A High-Precision, Lightweight Model for Sheep Face Recognition on the Mobile End

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