Harnessing the Power of Deep Learning to Save Animals

Mridula, B. V. S.; Bonde, Padma

doi:10.5120/ijca2017915864

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…To tackle this problem, the automation of the customs clearance system for specific animal species based on artificial intelligence technologies and object detection methods can be a good tool. Object detection (i.e., the identification of a specific area in an image) is widely used in video monitoring [ 5 , 6 , 7 ], medical diagnosis [ 8 ], and many other fields.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance

Jang

Lee

2021

Biology

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Owing to climate change and human indiscriminate development, the population of endangered species has been decreasing. To protect endangered species, many countries worldwide have adopted the CITES treaty to prevent the extinction of endangered plants and animals. Moreover, research has been conducted using diverse approaches, particularly deep learning-based animal and plant image recognition methods. In this paper, we propose an automated image classification method for 11 endangered parrot species included in CITES. The 11 species include subspecies that are very similar in appearance. Data images were collected from the Internet and built in cooperation with Seoul Grand Park Zoo to build an indigenous database. The dataset for deep learning training consisted of 70% training set, 15% validation set, and 15% test set. In addition, a data augmentation technique was applied to reduce the data collection limit and prevent overfitting. The performance of various backbone CNN architectures (i.e., VGGNet, ResNet, and DenseNet) were compared using the SSD model. The experiment derived the test set image performance for the training model, and the results show that the DenseNet18 had the best performance with an mAP of approximately 96.6% and an inference time of 0.38 s.

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Section: Introductionmentioning

confidence: 99%

Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance

Jang

Lee

2021

Biology

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The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning

Choe

Choi

Kim

2020

Mobile Information Systems

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Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

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Harnessing the Power of Deep Learning to Save Animals

Cited by 2 publications

References 2 publications

Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance

Multi-Class Parrot Image Classification Including Subspecies with Similar Appearance

The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning

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