A REVIEW THREAT OBJECT DETECTION IN X -RAY IMAGES USING SSD, R-FCN AND FASTER R -CNN

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dc.contributor.author Koçi, Jola
dc.date.accessioned 2025-01-24T12:16:46Z
dc.date.available 2025-01-24T12:16:46Z
dc.date.issued 2020-07-23
dc.identifier.uri http://dspace.epoka.edu.al/handle/1/2466
dc.description.abstract Baggage inspection for threat objects using X-ray images is a priority task that is in charge of making the risk of crime and terrorist attacks more reducible. Nowadays, the checking of baggage is based on a semi-automated system that consists of both human and also image detection. The main purpose of this thesis is to make the automatisation system more reliable. This task is mainly dependent on object detection models and algorithms. However, another part of this problem is mainly prone to the lack of the data. Furthermore, obtaining an X-ray image dataset with many types of threat objects is quite difficult. This is why this thesis is composed of two main parts: data stimulation and object detecting approaches. On the first part, due to the lack of data, an older dataset containing only four classes of threat objects are used as a base for the new objects to be stimulated into. In total, the newly simulated dataset contains seven types of threat objects consisting of: handguns, razor-blades, knife, shuriken, battery, wires and mortar. After generating the new data, they are processed and augmented by applying random types of rotations, flippings, zoomings etc. Once the images are processed, they are passed into transfer learning. Transfer learning consists of using predefined models for training. The models that are taken into consideration are: Single Shot Detector, Regions Fully Convolutional Network (R- FCN) and Faster R-CNN. These models are used by applying different techniques of feature extraction, such as: Inception-v2, MobileNet-v2 and ResNet101. Combining the object detection models and object detection architectures in total the images are trained and tested in five different approaches. In conclusion, the best detection was achieved by the combination of Faster-RCNN detection model and ResNet101 feature extractor by 87.58% ± 0.75 accuracy. en_US
dc.language.iso en en_US
dc.subject Object Detection, Threat objects, X-ray Images, Data Stimulation, Data augmentation en_US
dc.title A REVIEW THREAT OBJECT DETECTION IN X -RAY IMAGES USING SSD, R-FCN AND FASTER R -CNN en_US
dc.type Thesis en_US


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