Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5
The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at...
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doaj-bebba01b249d47448e7e568a92e336812021-03-31T23:04:23ZengMDPI AGAlgorithms1999-48932021-03-011411411410.3390/a14040114Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5Margrit Kasper-Eulaers0Nico Hahn1Stian Berger2Tom Sebulonsen3Øystein Myrland4Per Egil Kummervold5Capia AS, 9008 Tromsø, NorwayCapia AS, 9008 Tromsø, NorwayCapia AS, 9008 Tromsø, NorwayCapia AS, 9008 Tromsø, NorwayCapia AS, 9008 Tromsø, NorwayCapia AS, 9008 Tromsø, NorwayThe proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.https://www.mdpi.com/1999-4893/14/4/114object detectionYOLOv5CNNsvehicle detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Margrit Kasper-Eulaers Nico Hahn Stian Berger Tom Sebulonsen Øystein Myrland Per Egil Kummervold |
spellingShingle |
Margrit Kasper-Eulaers Nico Hahn Stian Berger Tom Sebulonsen Øystein Myrland Per Egil Kummervold Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5 Algorithms object detection YOLOv5 CNNs vehicle detection |
author_facet |
Margrit Kasper-Eulaers Nico Hahn Stian Berger Tom Sebulonsen Øystein Myrland Per Egil Kummervold |
author_sort |
Margrit Kasper-Eulaers |
title |
Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5 |
title_short |
Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5 |
title_full |
Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5 |
title_fullStr |
Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5 |
title_full_unstemmed |
Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5 |
title_sort |
short communication: detecting heavy goods vehicles in rest areas in winter conditions using yolov5 |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2021-03-01 |
description |
The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection. |
topic |
object detection YOLOv5 CNNs vehicle detection |
url |
https://www.mdpi.com/1999-4893/14/4/114 |
work_keys_str_mv |
AT margritkaspereulaers shortcommunicationdetectingheavygoodsvehiclesinrestareasinwinterconditionsusingyolov5 AT nicohahn shortcommunicationdetectingheavygoodsvehiclesinrestareasinwinterconditionsusingyolov5 AT stianberger shortcommunicationdetectingheavygoodsvehiclesinrestareasinwinterconditionsusingyolov5 AT tomsebulonsen shortcommunicationdetectingheavygoodsvehiclesinrestareasinwinterconditionsusingyolov5 AT øysteinmyrland shortcommunicationdetectingheavygoodsvehiclesinrestareasinwinterconditionsusingyolov5 AT peregilkummervold shortcommunicationdetectingheavygoodsvehiclesinrestareasinwinterconditionsusingyolov5 |
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