Accident vehicle types classification: a comparative study between different deep learning models
Classifying and finding type of individual vehicles within an accident image are considered difficult problems. This research concentrates on accurately classifying and recognizing vehicle accidents in question. The aim to provide a comparative analysis of vehicle accidents. A number of network topo...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-03-01.
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| Online Access: | Get fulltext |
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| LEADER | 02525 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs23823_14720 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Anwer, Mardin A. |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Shareef, Shareef M. |e author |
| 700 | 1 | 0 | |a Ali, Abbas M. |e author |
| 245 | 0 | 0 | |a Accident vehicle types classification: a comparative study between different deep learning models |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-03-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23823 | ||
| 520 | |a Classifying and finding type of individual vehicles within an accident image are considered difficult problems. This research concentrates on accurately classifying and recognizing vehicle accidents in question. The aim to provide a comparative analysis of vehicle accidents. A number of network topologies are tested to arrive at convincing results and a variety of matrices are used in the evaluation process to identify the best networks. The best two networks are used with faster recurrent convolution neural network (Faster RCNN) and you only look once (YOLO) to determine which network will identifiably detect the location and type of the vehicle. In addition, two datasets are used in this research. In consequence, experiment results show that MobileNetV2 and ResNet50 have accomplished higher accuracy compared to the rest of the models, with 89.11% and 88.45% for the GAI dataset as well as 88.72% and 89.69% for KAI dataset, respectively. The findings reveal that the ResNet50 base network for YOLO achieved higher accuracy than MobileNetV2 for YOLO, ResNet50 for Faster RCNN with 83%, 81%, and 79% for GAI dataset and 79%, 78% and 74% for KAI dataset. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Accident recognition; Deep learning; GAI and KAI datasets; Transfer learning; Vehicle accidents image classification | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 21, No 3: March 2021; 1474-1484 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v21.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23823/14720 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23823/14720 |z Get fulltext |