Classification of trucks using camera data

Show simple item record Mokone, O. De saxe, C.C. 2021-11-02T09:24:39Z 2021-11-02T09:24:39Z 2021
dc.description Papers presented virtually at the 39th International Southern African Transport Conference on 05 -07 July 2021
dc.description.abstract Understanding the precise movements of different commodities on South African roads can help in not only describing the logistics sector more accurately, but also in the planning of road infrastructure maintenance and investment. Truck combinations can be classified into several classes broadly associated with different commodity groups, including tautliners, tankers, flatbeds (general freight) and flatbed (containerised freight). Current truck classification systems in South Africa can classify trucks by number of axles and vehicle mass but are unable to determine the combination type and hence commodity group. Video data allows for truck combinations to be classified in more detail using image-based classifiers. The latest developments in deep learning algorithms have made it possible for accurate classification of vehicle types using camera data. A CCTV camera feed of a section of the N3 was provided by the South African National Roads Agency Limited (SANRAL) and was used as a case study to develop a proof-of-concept classifier for tautliner and tanker truck combinations, using a transfer learning approach and the pre-trained ResNet50 classifier. The results indicate good accuracy based on relatively small datasets. Future work will focus on further optimisation and investigating the training dataset requirements in more detail.
dc.format.extent 11 pages
dc.format.medium PDF
dc.language.iso en
dc.publisher Southern African Transport Conference 2021
dc.rights Southern African Transport Conference 2021
dc.subject Heavy vehicles
dc.subject Trucks
dc.subject Logistics
dc.title Classification of trucks using camera data
dc.type Article

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