A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models
Date
2021
Authors
Ibraheam, Mai
Li, Kin Fun
Gebali, Fayez
Sielecki, Leonard E.
Journal Title
Journal ISSN
Volume Title
Publisher
AI
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.
Description
We gratefully acknowledge the support by the British Columbia Ministry of
Transportation and Infrastructure.
Keywords
deep learning, convolutional neural network (CNN), region-based CNN (R-CNN) models, Deformable CNN (D-CNN), animal species detection
Citation
Ibraheam, M., Li, K. F., Gebali, F., & Sielecki, L. E. (2021). A performance comparison and enhancement of animal species detection in images with various RCNN models. AI, 2(4), 552-577. doi.org/10.3390/ai2040034.