A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks
| dc.contributor.advisor | Valles, Damian | |
| dc.contributor.author | Jaradat, Farah Bilal | |
| dc.contributor.committeeMember | Stapleton, William | |
| dc.contributor.committeeMember | Koutitas, George | |
| dc.date.accessioned | 2019-12-05T13:26:02Z | |
| dc.date.available | 2019-12-05T13:26:02Z | |
| dc.date.issued | 2019-12 | |
| dc.description.abstract | In this work, an approach is proposed to detect people and pets trapped in burning sites. This will be done in a manner that will ensure the safety of firefighters and accelerate the rescue process of victims. The proposed work suggests detecting victims in fire situations autonomously, through a deep learning technique using the Convolutional Neural Network (CNN) model. Research work in the field of firefighters’ assistance suggest using infrared (IR) sensors, which are widely used in this field due to their functionality in fire situations, since such cases are associated with high levels of smoke, that limit the vision as well as the feature of white light cameras since the smoke particles, reflect the light. Thus, thermal sensors found to be a convenient choice for gathering data for detecting victims in the burning site without distortion of smoke. Despite the huge progress is the field of firefighting assistance due to the huge technological advancement, however, deciding if victims exist inside the burning structure without human interference has remained a challenge. Here deep learning model is introduced to identify victims’ appearances in IR images. What distinguishes this work is leveraging IR imaging combined with deep learning models, which were proved to be the state-of-the-art techniques in the field of people detection. The proposed method is extended to detect pets in the burning sites. The thesis work provides a deep learning approach that speeds up and facilitates the process of rescuing victims in vague burning sites layouts; hence, more lives would be saved. The objective of the CNN development is to classify input images sent from the burning site into one of three outcomes classes: “<i>people</i>,” “<i>pets</i>,” and “<i>no victims</i>.” First responders can leverage this information to define their priorities regarding the locations they should target first. Deep learning is a popular method for human detection in the IR image, and it is applied extensively to the field of pedestrian detection and surveillance. However, the literature scarce for applications in fire assistance fields. One reason behind this, is the unavailability of public IR datasets, including infrared images of people and pets. Therefore, a dataset was developed to accomplish the task of human detection in IR images. Furthermore, the dataset was processed to mimic high-temperature environments as in building on fire situations. In this thesis, a cascaded CNN architecture approach was implemented and benchmarked, and the cascaded model consists of two stages to accomplish the task of victim detection in IR images. | |
| dc.description.department | Engineering | |
| dc.format | Text | |
| dc.format.extent | 113 pages | |
| dc.format.medium | 1 file (.pdf) | |
| dc.identifier.citation | Jaradat, F. B. (2019). <i>A victims detection approach for burning building sites using convolutional neural networks</i> (Unpublished thesis). Texas State University, San Marcos, Texas. | |
| dc.identifier.uri | https://hdl.handle.net/10877/9010 | |
| dc.language.iso | en | |
| dc.subject | Convolutional neural networks | |
| dc.subject | Firefighting assistance | |
| dc.subject | Image recognition | |
| dc.subject.lcsh | Fire extinction--Technology | |
| dc.subject.lcsh | Computer vision | |
| dc.subject.lcsh | Pattern recognition systems | |
| dc.title | A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks | |
| dc.type | Thesis | |
| thesis.degree.department | Engineering | |
| thesis.degree.discipline | Engineering | |
| thesis.degree.grantor | Texas State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |