Knowing Your Dog Breed: Identifying a Dog Breed with Deep Learning

Borwarnginn, Punyanuch; Kusakunniran, Worapan; Karnjanapreechakorn, Sarattha; Thongkanchorn, Kittikhun

doi:10.1007/s11633-020-1261-0

Cited by 15 publications

(7 citation statements)

References 21 publications

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“…The identification of dog breeds is essential for understanding dog health problems and scarlet action behavior. Borwarnginn et al 17 proposed a dog breed identification classification model by retraining dog breed data sets and pretrained CNNs, and the proposed model recorded an accuracy of 89%. Nagy and Korondi 18 proposed a model that recognizes and analyzes actual dog behavior patterns using deep learning to implement dog behavior in robots.…”

Section: Discussionmentioning

confidence: 99%

Developing a diagnosis model for dry eye disease in dogs using object detection

Kim

Han

Chun

et al. 2022

Sci Rep

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The purpose of this study was to develop an object detection method for the diagnosis of dry eye disease (DED) in dogs. To this end, a methodology was designed to evaluate ocular surface video images using the YOLOv5 model, which is an object detection algorithm that has been widely used because of its simple network structure and fast detection speed. Because the cornea is a transparent organ, an illuminator plate with grid squares was used to provide grid lines, which were analyzed as the reflected straight lines of the light source representing the precorneal tear film (PTF) stability. The original video consisted of the number of 12 normal images(normal, $$n$$ n = 17) and the number of 15 abnormal images(abnormal, $$n$$ n = 17), converted to JPEG images for labeling, learning, and model validation. The labeled image data were divided into a training image data set (normal, $$n$$ n = 15,276; abnormal, $$n$$ n = 26,196) to a validation image data set (normal, $$n$$ n = 6546; abnormal, $$n$$ n = 11,228). As a result of the experiment, the mean average precision ($$mAP$$ mAP ) achieved 0.995. This study proposes a method to effectively determine ocular surface status in dogs by using YOLOv5 and concludes that an object detection model can be used in the veterinary field.

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Section: Discussionmentioning

confidence: 99%

Developing a diagnosis model for dry eye disease in dogs using object detection

Kim

Han

Chun

et al. 2022

Sci Rep

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“…Dog breed classification is a classic problem in computer vision, and models trained on images from a large compendium (ImageNet) have been evaluated for their performance in classifying photographs of purebred dogs. The model NASNet shows particularly robust performance [37][38][39] . Therefore, we used NASNet predictions to complement the DTC genetic testing results by predicting breeds based exclusively on photographs.…”

Section: Methodsmentioning

confidence: 98%

Is a Picture Worth 1,000 SNPs? Effects of User-Submitted Photographs on Ancestry Estimates from Direct-to-Consumer Canine Genetic Tests

Rando

Graim

Hampikian

et al. 2023

Preprint

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Objective: To evaluate whether the breed ancestry predictions of direct-to-consumer (DTC) genetic tests for dogs are influenced by the user-provided photograph. Animals: Twelve pet dogs considered purebred (i.e., registered with a breed organization) representing twelve different breeds. Methods: Six buccal swabs per dog were collected by the owners and submitted to six DTC genetic testing companies. The experimenters registered each sample with the company. For half of the dogs, the registration included a photograph of the DNA donor. For the other half of the dogs, photographs were swapped between dogs. Analysis of the DNA and breed ancestry prediction was conducted by each company. Each company's breed predictions were evaluated to assess whether the condition (i.e., matching versus shuffled photograph) affected the odds of identifying the DNA donor's registered breed. A convolutional neural network was also used to predict breed based solely on the photograph as a positive control. Results: Five of the six tests always produced results that included the registered breed. One test and the convolutional neural network were unlikely to identify the registered breed and frequently returned results that included the breed in the photograph. This result suggests that one test on the market is relying on the photograph more than the DNA sample. Additionally, differences in the predictions made across all tests underscore the challenge of identifying breed ancestry, even in purebred dogs. Clinical Relevance: Veterinarians are likely to encounter patients who have conducted DTC genetic testing and may find themselves in the position of explaining genetic test results that they did not order. This systematic comparison of tests on the market provides context for interpreting unexpected results from consumer-grade DTC genetic testing kits.

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“…The accuracy evaluation revealed that the Inception ResNet V2 algorithm achieved the highest accuracy, ranging from 88.40% to 93.30% [26]. Another application focused on digital photo classification to distinguish dog breeds using deep learning, demonstrating an accuracy of 89.92% [36]. Lastly, digital photo classification was presented for distinguishing between cats and dogs using CNN, achieving an accuracy of 93.67% for the training data and 90.10% for the testing data [37].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Saeliw,

Hualkasin,

Puttinaovarat

2024

TEM Journal

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One of the challenges encountered by farmers engaged in livestock farming is the menace posed by stray or ownerless dogs, causing harm to the animals being raised on the farm. This not only adversely affects the health of the animals but also impacts the overall cost associated with their upbringing. Consequently, this research introduces the development of a sophisticated system aimed at preventing threats and intrusions by dogs that pose harm to farm animals. The system leverages Internet of Things (IoT) technology and employs Machine Learning algorithms, specifically Convolutional Neural Network, for real-time tracking and monitoring. The research findings reveal that the developed system demonstrates a high level of efficiency, swiftly and accurately classifying animals entering areas equipped with cameras, achieving an impressive accuracy rate of 92.54%. Furthermore, the system is equipped to promptly notify users and emit deterrent sounds to repel dogs entering the monitored area, enhancing its effectiveness in safeguarding livestock and optimizing farm management practices.

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Knowing Your Dog Breed: Identifying a Dog Breed with Deep Learning

Cited by 15 publications

References 21 publications

Developing a diagnosis model for dry eye disease in dogs using object detection

Developing a diagnosis model for dry eye disease in dogs using object detection

Is a Picture Worth 1,000 SNPs? Effects of User-Submitted Photographs on Ancestry Estimates from Direct-to-Consumer Canine Genetic Tests

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

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