التفاصيل البيبلوغرافية
| العنوان: |
Deep learning-based scheduler for efficient object detection in a distributed architecture ; Djupinlärningsbaserad schemaläggare för effektiv objektdetektering i en distribuerad arkitektur |
| المؤلفون: |
Patkhullaev, Davron |
| بيانات النشر: |
KTH, Sannolikhetsteori, matematisk fysik och statistik |
| سنة النشر: |
2023 |
| المجموعة: |
Royal Inst. of Technology, Stockholm (KTH): Publication Database DiVA |
| مصطلحات موضوعية: |
Object detection, Offloading, Detection failure, Objektdetektering, Avlastning, Detektionsfel, Other Mathematics, Annan matematik |
| الوصف: |
Object detection (OD) is a computer vision problem that involves the identification and localization of objects within an image or video stream. 5G and edge computing technologies have enabled distributed OD systems to operate more efficiently. This thesis addresses the challenge of improving an existing edge-assisted OD pipeline, developed at the Sensing and Perception team, Ericsson Research. The existing pipeline uses support vector machines (SVM)-based classifier in order to identify images where lightweight OD has failed, and calculates an introspective score based on the classifier. It combines two failure scores, i.e., the introspective score with comparison based score so-called, a golden score. Combining is done by taking the weighted average between the scores that results in detection failure metric, (DFM), which is then used to offload OD from a resource-constraint device to a more powerful device (edge). There is room for improvement, mainly in two areas. Firstly, SVM is a simple classifier and requires features from OD to infer OD failures. Secondly, the pipeline does not consider end-to-end latency, therefore it worsens significantly in degraded network conditions. Due to its significance and degree of difficulty, this problem is important to tackle and appropriate topic for a Master's thesis. To solve the first problem, this thesis proposes a deep learning-based detection failure classifier that replaces the previous SVM-based classifier in order to identify images where lightweight OD has failed. For the second problem, several approaches to make offloading decision are proposed in order to consider end-to-end latency as well as network conditions by combining two failure scores with a new latency score. The effectiveness of the proposed methods is then tested against the ImageNet Large Scale Visual Recognition Challenge 2017 (ILSVRC2017) VID dataset using several accuracy metrics and end-to-end latency. Experimental evaluation shows that the final proposed method significantly enhances the ... |
| نوع الوثيقة: |
bachelor thesis |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
TRITA-SCI-GRU; 2023:473 |
| الاتاحة: |
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-356336 |
| Rights: |
info:eu-repo/semantics/openAccess |
| رقم الانضمام: |
edsbas.8A5A05BA |
| قاعدة البيانات: |
BASE |