E-Scooter Benchmark: Benchmarking the Deep Learning Techniques for Object Detection in E-scooters

1. Installation

• Create a conda environment: conda create -n escooters python=3.12 -y
• Active the virtual environment: conda activate escooters
• Install requirements: pip install -r requirements.txt

2. Preparing the Dataset

2.1 Dataset Preparation

• Download the data (https://zenodo.org/records/10578641).
• Run the script to convert the labeled data into YOLO format: python yolov5/commons/labelme2yolo.py

3. Training and Testing

• Download the pre-trained models from the official YOLO websites and unzip them to the corresponding folders. For example, you need to put the yolov3.pt, yolov3-spp.pt and yolov3-tiny.pt under the YOLOV3/ folder.
• You can run the 0st data folder, we can run:bash -i train.sh.
• To test the models, we can run: bash -i test.sh.

4. Performance

The YOLO algorithms[1-6] used for our experiments are not maintained by us, please give credit to the authors of the YOLO algorithms[1-6].

Video Demos

The video demos can be accessed at [Demo]

Citation

If you find the models and or the dataset useful, consider citing the following article:

Coming soon

Reference

• [1-1] YOLOv3: Redmon, Joseph, and Ali Farhadi. "Yolov3: An incremental improvement." arXiv preprint arXiv:1804.02767 (2018).
• [1-2] YOLOv3 Implementation: https://github.com/ultralytics/yolov3.
• [2-1] YOLOv4: Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "Yolov4: Optimal speed and accuracy of object detection." arXiv preprint arXiv:2004.10934 (2020).
• [2-2] YOLOv4 Implementation: https://github.com/WongKinYiu/PyTorch_YOLOv4.
• [3-1] YOLOv5: None
• [3-2] YOLOv5 Implementation: https://github.com/ultralytics/yolov5.
• [4-1] YOLOv6: Li, Chuyi, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke et al. "YOLOv6: A single-stage object detection framework for industrial applications." arXiv preprint arXiv:2209.02976 (2022).
• [4-2] YOLOv6 Implementation: https://github.com/meituan/YOLOv6.
• [5-1] YOLOv7: Wang, Chien-Yao, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464-7475. 2023.
• [5-2] YOLOv7 Implementation: https://github.com/WongKinYiu/yolov7
• [6-1] YOLOv8 Implementation: https://github.com/ultralytics/ultralytics