Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/64106
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.date.accessioned | 2020-11-18T12:01:16Z | - |
dc.date.available | 2020-11-18T12:01:16Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Cassar Ellis, J. (2020). A study on the effect of target object size in object detection (Bachelor's dissertation). | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/64106 | - |
dc.description | B.SC.ICT(HONS)ARTIFICIAL INTELLIGENCE | en_GB |
dc.description.abstract | Recent years have seen an impressive increase in the efficiency and accuracy of object detection models through the use of Region-based Convolutional Neural Networks (RCNN). Studies have been carried out to improve object detection models. However, the detection of small objects still poses numerous challenges for the said models. Small object detection is considered as one of the biggest challenges in object detection for several reasons. One in particular is due to the resizing of the feature maps within the pooling stage resulting in the loss of the small target object’s features. Moreover, accuracy diminishes as these networks struggle to distinguish between foreground and complex backgrounds such as rough terrain. An experiment was designed to investigate a selection of deep neural networks and analyse the effects that the distance between the capturing device and the target object. The set up requires a custom dataset, containing object instances that are identifiable by the selected models, each with an object instance at a varying distance. The COCO dataset was selected as the training and benchmarking dataset in this experiment. Following the selection of a benchmarking dataset, the custom dataset used to test for the effect of target object size was obtained. The custom dataset was prepared in a controlled environment set up with pre-established distance measurements and multiple small objects placed in a non-complex background for detection. By varying the range of distances, size of the object instance decreases, producing a lower amount of features. The selection of state-of-the-art object detection models include YOLO, EfficientDet and Detectron2 which are all pre-trained on the COCO dataset. To evaluate each model, a series of baseline tests were initially carried out to ensure that each model could correctly identify the object instance that is being used as a ground truth. Once the baselines were established, the custom dataset was applied and the results were extracted. Results show that EfficientDet produces an average FBeta measure of 67.2%, a 20.7% increase from the second best performing model for the custom dataset when measuring over all distances. However, it was interesting to note that accuracy falls off once the object instance exceeds five or more meters, with the object instance going either undetected or incorrectly labelled. | en_GB |
dc.language.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Pattern recognition systems | en_GB |
dc.subject | Neural networks (Computer science) | en_GB |
dc.subject | Data sets | en_GB |
dc.title | A study on the effect of target object size in object detection | en_GB |
dc.type | bachelorThesis | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.publisher.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Information and Communication Technology. Department of Artificial Intelligence | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Cassar Ellis, Jacob | - |
Appears in Collections: | Dissertations - FacICT - 2020 Dissertations - FacICTAI - 2020 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
20BITAI003 - Cassar Ellis Jacob.pdf Restricted Access | 81.46 MB | Adobe PDF | View/Open Request a copy |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.