A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models

Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certain target classes in a given image and assign each...
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Autor*in:	Mai Ibraheam [verfasserIn] Kin Fun Li [verfasserIn] Fayez Gebali [verfasserIn] Leonard E. Sielecki [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	deep learning convolutional neural network (CNN) region-based CNN (R-CNN) models Deformable CNN (D-CNN) animal species detection

Übergeordnetes Werk:	In: AI - MDPI AG, 2020, 2(2021), 4, Seite 552-577
Übergeordnetes Werk:	volume:2 ; year:2021 ; number:4 ; pages:552-577

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/ai2040034

Katalog-ID:	DOAJ016644417

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callnumber-first	Q - Science
author	Mai Ibraheam
spellingShingle	Mai Ibraheam misc QA75.5-76.95 misc deep learning misc convolutional neural network (CNN) misc region-based CNN (R-CNN) models misc Deformable CNN (D-CNN) misc animal species detection misc Electronic computers. Computer science A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models
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topic_title	QA75.5-76.95 A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models deep learning convolutional neural network (CNN) region-based CNN (R-CNN) models Deformable CNN (D-CNN) animal species detection
topic	misc QA75.5-76.95 misc deep learning misc convolutional neural network (CNN) misc region-based CNN (R-CNN) models misc Deformable CNN (D-CNN) misc animal species detection misc Electronic computers. Computer science
topic_unstemmed	misc QA75.5-76.95 misc deep learning misc convolutional neural network (CNN) misc region-based CNN (R-CNN) models misc Deformable CNN (D-CNN) misc animal species detection misc Electronic computers. Computer science
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title	A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models
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title_full	A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models
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title_sort	performance comparison and enhancement of animal species detection in images with various r-cnn models
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title_auth	A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models
abstract	Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certain target classes in a given image and assign each object to a corresponding class label. These techniques proceed differently in network architecture, training strategy and optimization function. In this paper, we focus on animal species detection as an initial step to mitigate the negative impacts of wildlife–human and wildlife–vehicle encounters in remote wilderness regions and on highways. Our goal is to provide a summary of object detection techniques based on R-CNN models, and to enhance the performance of detecting animal species in accuracy and speed, by using four different R-CNN models and a deformable convolutional neural network. Each model is applied on three wildlife datasets, results are compared and analyzed by using four evaluation metrics. Based on the evaluation, an animal species detection system is proposed.
abstractGer	Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certain target classes in a given image and assign each object to a corresponding class label. These techniques proceed differently in network architecture, training strategy and optimization function. In this paper, we focus on animal species detection as an initial step to mitigate the negative impacts of wildlife–human and wildlife–vehicle encounters in remote wilderness regions and on highways. Our goal is to provide a summary of object detection techniques based on R-CNN models, and to enhance the performance of detecting animal species in accuracy and speed, by using four different R-CNN models and a deformable convolutional neural network. Each model is applied on three wildlife datasets, results are compared and analyzed by using four evaluation metrics. Based on the evaluation, an animal species detection system is proposed.
abstract_unstemmed	Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certain target classes in a given image and assign each object to a corresponding class label. These techniques proceed differently in network architecture, training strategy and optimization function. In this paper, we focus on animal species detection as an initial step to mitigate the negative impacts of wildlife–human and wildlife–vehicle encounters in remote wilderness regions and on highways. Our goal is to provide a summary of object detection techniques based on R-CNN models, and to enhance the performance of detecting animal species in accuracy and speed, by using four different R-CNN models and a deformable convolutional neural network. Each model is applied on three wildlife datasets, results are compared and analyzed by using four evaluation metrics. Based on the evaluation, an animal species detection system is proposed.
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title_short	A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models
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