An Accurate and Fast Animal Species Detection System for Embedded Devices
| dc.contributor.author | Ibraheam, Mai Mahmoud | |
| dc.contributor.supervisor | Li, Kin Fun | |
| dc.contributor.supervisor | Gebali, Fayez | |
| dc.date.accessioned | 2023-12-06T19:25:04Z | |
| dc.date.available | 2023-12-06T19:25:04Z | |
| dc.date.copyright | 2023 | en_US |
| dc.date.issued | 2023-12-06 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Doctor of Philosophy Ph.D. | en_US |
| dc.description.abstract | Object detection is one of the vital and challenging tasks in the field of computer vision. It supports a wide range of applications in real life, such as surveillance, autonomous driving, and medical diagnostics. Object detection techniques aim to identify and localize objects of certain target classes within an image and assign each object to a corresponding class label. These techniques vary in their network architecture, training strategy, and optimization function. In this dissertation, an investigation into object detection is presented, with a specific emphasis on animal species detection. The research aims to mitigate the negative impacts of wildlife-human conflicts (WHCs) and wildlife-vehicle collisions (WVCs), particularly in remote wilderness regions/trails, urban areas/backyards, and on highways. Our goal is to enhance the accuracy and speed of animal species detection to ensure safer environments for both humans and wildlife. The research involves a comprehensive analysis of object detection techniques based on R-CNN models. Four different R-CNN models and a deformable convolutional neural network are applied on three wildlife datasets, and results are evaluated using four metrics. This comprehensive analysis informs the proposal of a novel animal species detection system. The results illustrate the system's high accuracy in distinguishing between different object categories such as animals, humans, and vehicles, as well as in identifying specific animal species. This work aims to develop an automated labelling and annotation system that eliminates the need for human intervention, thereby saving time and costs. Furthermore, it seeks to contribute to the development of robust and reliable systems which can be applied to various aspects of biological sciences, such as wildlife monitoring, conservation, and management. A key proposal of the research is to develop WHCs and WVCs real-time mitigation systems based on a lightweight animal species detection model (M-YOLO) derived from YOLOv2. Multi-level features merging is employed by adding a new pass-through layer to improve the feature extraction ability and accuracy of YOLOv2. Moreover, the two repeated 3 × 3 convolutional layers in the seventh block of the YOLOv2 architecture are removed to reduce computational complexity, and thus increase detection speed without reducing accuracy. Animal species detection methods based on regular Convolutional Neural Networks (CNNs) have been widely applied; however, these methods are difficult to adapt to geometric variations of animals in images. Thus, a modified YOLOv2 with the addition of deformable convolutional layers (DCLs) was proposed to resolve this issue. Our experimental results show that the proposed model outperforms the original YOLOv2 by 5.0% in accuracy and 12.0% in speed. Furthermore, our analysis shows that the proposed model is more suitable for deployment on embedded devices than YOLOv3 and YOLOv4. To further enhance the M-YOLO model and achieve real-time alerts on low-power and resource-constrained devices, the research proposes the integration of two key ideas: the Motion-selective Control Frames (MCF) algorithm and a parallel processing technique. These enhancements aim to minimize the detection processing delay and power consumption, which are crucial for the efficient operation of low-power, computationally limited embedded devices. Importantly, these improvements are achieved while maintaining detection accuracy. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/15675 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | embedded devices | en_US |
| dc.subject | animal detection | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | AI | en_US |
| dc.subject | object detection | en_US |
| dc.subject | parallel processing | en_US |
| dc.subject | motion detection | en_US |
| dc.title | An Accurate and Fast Animal Species Detection System for Embedded Devices | en_US |
| dc.type | Thesis | en_US |