The animal-vehicle collision has been a topic of concern for years, especially in North America. To mitigate the problem, this thesis focuses on animal detection based on the onboard camera for intelligent vehicles.
In the domain of image classification and object detection, the methods of shape matching and local feature crafting have reached the technical plateau for decades. The development of Convolutional Neural Network (CNN) brings a new breakthrough. The evolution of CNN architectures has dramatically improved the performance of image classification. Effective frameworks on object detection through CNN structures are thus boosted. Notably, the family of Region-based Convolutional Neural Networks (R-CNN) perform well by combining region proposal with CNN. In this thesis, we propose to apply a new region proposal method|Maximally Stable Extremal Regions (MSER) in Fast R-CNN to construct the animal detection framework.
MSER algorithm detects stable regions which are invariant to scale, rotation and viewpoint changes. We generate regions of interest by dealing with the result of MSER algorithm in two ways: by enclosing all the pixels from the resulted pixel-list with a minimum enclosing rectangle (the PL MSER) and by fitting the resulted elliptical region to an approximate box (the EL MSER). We then preprocess the bounding boxes of PL MSER and EL MSER to improve the recall of detection. The preprocessing steps consist of filtering out undesirable regions by aspect ratio model, clustering bounding boxes to merge the overlapping regions, modifying and then enlarging the regions to cover the entire animal. We evaluate the two region proposal methods by the measurement of recall over IOU-threshold curve. The proposed MSER method can cover the expected regions better than Edge Boxes and Region Proposal Network (RPN) in Faster R-CNN. We apply the MSER region proposal method to the framework of R-CNN and Fast R-CNN. The experiments on the animal database with moose, deer, elk, and horses show that Fast R-CNN with MSER achieves better accuracy and faster speed than R-CNN with MSER. Concerning the two ways of MSER, the experimental results show that PL MSER is faster than EL MSER and EL MSER gains higher precision than PL MSER. Also, by altering the structure of network used in Fast R-CNN, we verify that network stacking more layers achieves higher accuracy and recall.
In addition, we compare the Fast R-CNN framework using MSER region proposal with the state-of-the-art Faster R-CNN by evaluating the experimental results of on our animal database. Using the same CNN structure, the proposed Fast R-CNN with MSER gains a higher average accuracy of the animal detection 0.73, compared to 0.42 of Faster R-CNN. In terms of detection quality, the proposed Fast R-CNN with MSER achieves better IoU histogram than that of Faster R-CNN.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/38045 |
| Date | 29 August 2018 |
| Creators | Zhao, Weihong |
| Contributors | Boukerche, Azzedine |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.002 seconds