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.
Highlights
Object detection has been widely studied to identify objects within an image to a predefined set of object classes and where these objects are in the image using bounding boxes [1]
An object detection framework using a Region-based Convolutional Neural Networks (CNNs) (R-CNN) model can be divided into four stages: (i) region of interest (RoI) selection, known as region proposals; (ii) features extraction for each region proposal using CNN; (iii) region classification; and (iv) object localization by combining overlapped region proposals into a single bounding box around each detected object using bounding box regression [7,8,9,10,11]
The effect of adding these convolutional layers to the four R-CNN detectors as we investigated by evaluating the performance of these detectors using three animal datasets
Summary
Object detection has been widely studied to identify objects within an image to a predefined set of object classes (object identification) and where these objects are in the image (object localization) using bounding boxes [1]. An object detection framework using a Region-based CNN (R-CNN) model can be divided into four stages: (i) region of interest (RoI) selection, known as region proposals; (ii) features extraction for each region proposal using CNN; (iii) region classification (which objects are in each proposal); and (iv) object localization by combining overlapped region proposals into a single bounding box around each detected object using bounding box regression [7,8,9,10,11]. All these processes are time consuming, making R-CNN slow
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