A New Approach to Detect Driver Distraction to Ensure Traffic Safety and Prevent Traffic Accidents: Image Processing and MCDM
Abstract
:1. Introduction
2. Literature Review
Author | Method/Algorithm | Number of Classes | Dataset | mAP |
---|---|---|---|---|
[23] | CDCNN and VGG-16 | 10 | StateFarm | 99.73% |
[24] | ResNeXt-34, ResNeXt-50, VGG-16, and VGG-19 | 2 | Author’s | 92.88% |
[25] | AlexNet, Inception V3, VGG-16, and 3D-CNN | 10 | StateFarm | 98.05% |
[6] | E2DR/ResNet50 and VGG-16 | 10 | StateFarm | 92% |
[26] | AlexNet, VGG16, EfficientNet B0, Vanilla CNN, Modified DenseNet, and InceptionV3 + BiLSTM | 10 | StateFarm and AUC | 99.75% |
[27] | MobileNet, ResNet, Inception-v3, and DenseNet | 10 | StateFarm | 94.3% |
[28] | SqueezeNet | 10 | StateFarm | 99.93% |
[29] | VGG-16 and VGG-19 | 10 | StateFarm | 99.39% |
[30] | MobileNet-SSD, YOLOv3, and Faster R-CNN | 1 | Author’s | 99% |
3. Materials and Methods
3.1. Data Collection and Preparation
3.2. Analytic Hierarchy Process
- Step 1. Defining the decision problem and determining the criteria/options;
- Step 2. Creating the hierarchical structure;
- Step 3. Establishment of pairwise comparison matrices and comparison of criteria.
- Step 4. The formulation in Equation (1) is used to normalize the pairwise comparison matrices [40].
- Step 5. The formulation in Equation (2) is used to calculate the priority vector [41].
- Step 6. To test the AHP consistency, the Consistency Ratio (CR) is calculated using Equations (3) and (4) [38].
3.3. You Only Look Once (YOLO)
4. Results and Discussion
4.1. Analysis of AHP
4.2. Analysis of YOLO Algorithm
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Drive Activity Class | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|
Prioritization Weights | 0.0182 | 0.0115 | 0.0096 | 0.0106 | 0.0086 | 0.0106 | 0.0010 |
Drive Activity Class | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
Prioritization Weights | 0.0163 | 0.0585 | 0.0605 | 0.0681 | 0.0086 | 0.0576 | 0.0393 |
Drive Activity Class | 15 | 16 | 17 | 18 | 19 | 20 | 21 |
Prioritization Weights | 0.0873 | 0.1142 | 0.0979 | 0.0969 | 0.0701 | 0.0557 | 0.0096 |
Drive Activity Class | 22 | 23 | |||||
Prioritization Weights | 0.0086 | 0.0806 |
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Alemdar, K.D.; Çodur, M.Y. A New Approach to Detect Driver Distraction to Ensure Traffic Safety and Prevent Traffic Accidents: Image Processing and MCDM. Sustainability 2024, 16, 7642. https://doi.org/10.3390/su16177642
Alemdar KD, Çodur MY. A New Approach to Detect Driver Distraction to Ensure Traffic Safety and Prevent Traffic Accidents: Image Processing and MCDM. Sustainability. 2024; 16(17):7642. https://doi.org/10.3390/su16177642
Chicago/Turabian StyleAlemdar, Kadir Diler, and Muhammed Yasin Çodur. 2024. "A New Approach to Detect Driver Distraction to Ensure Traffic Safety and Prevent Traffic Accidents: Image Processing and MCDM" Sustainability 16, no. 17: 7642. https://doi.org/10.3390/su16177642