Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Real Time Detection and Recognition of Construction Vehicles: Using Deep Learning Methods
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2020 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Background. The driving conditions of construction vehicles and their surrounding environment is different from the traditional transportation vehicles. As a result,they face unique challenges while operating in the construction/evacuation sites.Therefore, there needs to be research carried-out to address these challenges while implementing autonomous driving, although the learning approach for construction vehicles is the same as for traditional transportation vehicles such as cars.

Objectives. The following objectives have been identified to fulfil the aim of this thesis work. To identify suitable and highly efficient CNN models for real-time object recognition and tracking of construction vehicles. Evaluate the classification performance of these CNN models. Compare the results among one another and present the results.

Methods. To answer the research questions, Literature review and Experiment have been identified as the appropriate research methodologies. Literature review has been performed to identify suitable object detection models for real-time object recognition and tracking. Following this, experiments have been conducted to evaluate the performance of the selected object detection models.

Results. Faster R-CNN model, YOLOv3 and Tiny-YOLOv3 have been identified from the literature review as the most suitable and efficient algorithms for detecting and tracking scaled construction vehicles in real-time. The classification performance of these algorithms has been calculated and compared with each other. The results have been presented.

Conclusions. The F1 score and accuracy of YOLOv3 has been found to be better amongst the algorithms, followed by Faster R-CNN. Therefore, it has been concluded that YOLOv3 is the best algorithm in the real-time detection and tracking of scaled construction vehicles. The results are similar to the classification performance comparison of these three algorithms provided in the literature.

Place, publisher, year, edition, pages
2020. , p. 66
Keywords [en]
Object detection and recognition, Deep Learning, Classification performance, YOLOv3, Construction Vehicles
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-19311OAI: oai:DiVA.org:bth-19311DiVA, id: diva2:1414033
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVADA Master Qualification Plan in Computer Science
Supervisors
Examiners
Available from: 2020-03-17 Created: 2020-03-11 Last updated: 2020-03-17Bibliographically approved

Open Access in DiVA

Real Time Detection...(7433 kB)89 downloads
File information
File name FULLTEXT02.pdfFile size 7433 kBChecksum SHA-512
55bc0adb245cb1059d2cae618fa56368bc966695d92121b3a817c42be9e3d42545b9426b40e8de834dd85fb760aa015dce9fa8863b2f668e4bad1e12b1e1a89b
Type fulltextMimetype application/pdf

By organisation
Department of Computer Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 89 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 44 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf