Multi-Bayesian Approach to Stochastic Feature Recognition in the Context of Road Crack Detection and Classification
| dc.contributor.author | Steckenrider, John J. | en |
| dc.contributor.committeechair | Furukawa, Tomonari | en |
| dc.contributor.committeemember | Parker, Robert G. | en |
| dc.contributor.committeemember | Abbott, A. Lynn | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2018-01-12T16:00:56Z | en |
| dc.date.available | 2018-01-12T16:00:56Z | en |
| dc.date.issued | 2017-12-04 | en |
| dc.description.abstract | This thesis introduces a multi-Bayesian framework for detection and classification of features in environments abundant with error-inducing noise. The approach takes advantage of Bayesian correction and classification in three distinct stages. The corrective scheme described here extracts useful but highly stochastic features from a data source, whether vision-based or otherwise, to aid in higher-level classification. Unlike many conventional methods, these features’ uncertainties are characterized so that test data can be correctively cast into the feature space with probability distribution functions that can be integrated over class decision boundaries created by a quadratic Bayesian classifier. The proposed approach is specifically formulated for road crack detection and characterization, which is one of the potential applications. For test images assessed with this technique, ground truth was estimated accurately and consistently with effective Bayesian correction, showing a 33% improvement in recall rate over standard classification. Application to road cracks demonstrated successful detection and classification in a practical domain. The proposed approach is extremely effective in characterizing highly probabilistic features in noisy environments when several correlated observations are available either from multiple sensors or from data sequentially obtained by a single sensor. | en |
| dc.description.abstractgeneral | Humans have an outstanding ability to understand things about the world around them. We learn from our youngest years how to make sense of things and perceive our environment even when it is not easy. To do this, we inherently think in terms of probabilities, updating our belief as we gain new information. The methods introduced here allow an autonomous system to think similarly, by applying a fairly common probabilistic technique to the task of perception and classification. In particular, road cracks are observed and classified using these methods, in order to develop an autonomous road condition monitoring system. The results of this research are promising; cracks are identified and correctly categorized with 92% accuracy, and the additional “intelligence” of the system leads to a 33% improvement in road crack assessment. These methods could be applied in a variety of contexts as the leading edge of robotics research seeks to develop more robust and human-like ways of perceiving the world. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.uri | http://hdl.handle.net/10919/81752 | en |
| dc.language.iso | en_US | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Bayesian classification | en |
| dc.subject | Crack detection | en |
| dc.subject | Road condition monitoring | en |
| dc.subject | Recursive Bayesian estimation | en |
| dc.subject | Stochastic features | en |
| dc.subject | Machine learning | en |
| dc.subject | Computer vision | en |
| dc.title | Multi-Bayesian Approach to Stochastic Feature Recognition in the Context of Road Crack Detection and Classification | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |