Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle
Abstract
:1. Introduction
- The exploitation of state-of-the-art deep learning methods for detecting and estimating the range of motorcycles from remote sensing data;
- The use of different data augmentation techniques, such as rotation, changing light, color space augmentation, mosaic images, and horizontal flip data augmentation, to improve performance in terms of motorcycle detection;
- The examination of the performance of eight variations of the YOLO algorithms for object detection in images acquired from a car;
- The proposal of the MD-TinyYOLOv4 algorithm, which uses data augmentation, K-means++ clustering to optimize anchor box predictions, training with the Mish activation function instead of current functions such as ReLU or Leaky-ReLU and the addition of a dense SPP (Spatial Pyramid Pooling) network to accurately extract more features for the better detection of motorcycles near cars;
- The evaluation of the performances of Monodepth1 and Monodepth2 using our dataset and refinement using a joint bilateral filter to generate a disparity map with better visual quality and range value estimation;
- The provision of sufficient visualization results in classifying the condition of a motorcycle in the image as a dangerous or normal situation.
2. Materials and Methods
2.1. Motorcycle Detection with MD-TinyYOLOv4
2.2. Monodepth for Depth Estimation
2.3. Disparity Map Refinement
2.4. Combining Bounding Boxes and Disparity Maps
2.5. Proposed Dataset
2.5.1. Dataset for Motorcycle Detection
2.5.2. Dataset for Motorcycle Range Estimation
3. Results
3.1. Evaluation Parameters
3.2. Evaluation Results
3.2.1. Proposed MD-TinyYOLOv4
3.2.2. Disparity and Depth Map Extracting
3.2.3. Evaluating the Proposed Algorithm at Different Distances and Conditions
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Camera | Specifications | Parameters |
---|---|---|
Mynt-Eye D1000-IR-120/Color | Resolution | 640 × 480 px |
Pixel size | 3.75 µm | |
Baseline | 120 mm | |
Focal Length | 2.45 mm | |
Visual Angle | D:121° H:105° V:58° | |
Radial distortion parameters * | k1 = −0.3066, k2 = 0.00861 | |
Tangential distortion parameters * | p1 = −0.0003, p2 = 0.0015 |
Version | Precision | Recall | F1 Score | Motorcycle Position | Time Forecast | |
---|---|---|---|---|---|---|
Close | Far | |||||
YOLOv1 | 0.64 | 0.53 | 0.579 | ✓ | × | 40 FPS |
YOLOv2 | 0.67 | 0.61 | 0.63 | ✓ | × | 40 FPS |
SSD512 | 0.68 | 0.66 | 0.60 | ✓ | × | 28 |
SSD300 | 0.71 | 0.78 | 0.77 | ✓ | × | 25 |
YOLOv3 | 0.69 | 0.75 | 0.77 | ✓ | × | 30 FPS |
YOLOv4 | 0.75 | 0.79 | 0.79 | ✓ | ✓ | 35 FPS |
Tiny-YOLOv1 | 0.3 | 0.43 | 0.35 | ✓ | × | 120 FPS |
Tiny-YOLOv2 | 0.45 | 0.48 | 0.46 | ✓ | × | 200 FPS |
Tiny-YOLOv3 | 0.60 | 0.59 | 0.63 | ✓ | × | 220 FPS |
Tiny-YOLOv4 | 0.7 | 0.6 | 0.64 | ✓ | ✓ | 240 FPS |
MD-TinyYOLOv4 | 0.81 | 0.79 | 0.79 | ✓ | ✓ | 240 FPS |
Disparity Map Model | Disparity Filter | Pretrained Weight | Distance RMSE (m) | Runtime |
---|---|---|---|---|
Monodepth1 | None | ImageNet | 0.8346 | 35 fps or 0.028 s |
Joint bilateral filter | 0.7739 | |||
Monodepth1 | None | KITTI | 0.4756 | 35 fps or 0.028 s |
Joint bilateral filter | 0.416 | |||
Monodepth2 | None | ImageNet | 0.6868 | 45 fps or 0.022 s |
Joint bilateral filter | 0.6061 | |||
Monodepth2 | None | KITTI | 0.3620 | 45 fps or 0.022 s |
Joint bilateral filter | 0.323 |
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Shabestari, Z.B.; Hosseininaveh, A.; Remondino, F. Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle. Remote Sens. 2023, 15, 5548. https://doi.org/10.3390/rs15235548
Shabestari ZB, Hosseininaveh A, Remondino F. Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle. Remote Sensing. 2023; 15(23):5548. https://doi.org/10.3390/rs15235548
Chicago/Turabian StyleShabestari, Zahra Badamchi, Ali Hosseininaveh, and Fabio Remondino. 2023. "Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle" Remote Sensing 15, no. 23: 5548. https://doi.org/10.3390/rs15235548