jkd2021/YOLOv5-with-Lane-Detection

This repository "Object and lane detection with yolov5 Model" is based on the open-source yolov5 model, and the function of lane detection is developed by some digital image processing methods, especially the Hough transformation. The yolov5 code, mostly references to ultralytics's repository (thanks to Ultralytics) https://github.com/ultralytics/yolov5 , but a small part of it is my own adjustment, and are subject to modification or deletion without notice. Use at your own risk.

Sample running of this integrated detection

YOLOv5 model's Performance

Figure Comparison with other Networks

### Pretrained Checkpoints [assets]: https://github.com/ultralytics/yolov5/releases Model |size
^{(pixels) |mAP^{val
0.5:0.95 |mAP^{test
0.5:0.95 |mAP^{val
0.5 |Speed
^{V100 (ms) | |params
^{(M) |FLOPS
^{640 (B)
--- |--- |--- |--- |--- |--- |---|--- |---
[YOLOv5s][assets] |640 |36.7 |36.7 |55.4 |**2.0** | |7.3 |17.0
[YOLOv5m][assets] |640 |44.5 |44.5 |63.3 |2.7 | |21.4 |51.3
[YOLOv5l][assets] |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4
[YOLOv5x][assets] |640 |**50.4** |**50.4** |**68.8** |6.1 | |87.7 |218.8
| | | | | | || |
[YOLOv5s6][assets] |1280 |43.3 |43.3 |61.9 |**4.3** | |12.7 |17.4
[YOLOv5m6][assets] |1280 |50.5 |50.5 |68.7 |8.4 | |35.9 |52.4
[YOLOv5l6][assets] |1280 |53.4 |53.4 |71.1 |12.3 | |77.2 |117.7
[YOLOv5x6][assets] |1280 |**54.4** |**54.4** |**72.0** |22.4 | |141.8 |222.9
| | | | | | || |
[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |- |-}}}}}}}

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

$ pip install -r requirements.txt

Tutorials

see repository https://github.com/ultralytics/yolov5

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

Google Colab and Kaggle notebooks with free GPU:
Google Cloud Deep Learning VM. See GCP Quickstart Guide
Amazon Deep Learning AMI. See AWS Quickstart Guide
Docker Image. See Docker Quickstart Guide

Inference

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

$ python detect.py --source 0  # webcam
                            file.jpg  # image 
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            'https://youtu.be/NUsoVlDFqZg'  # YouTube video
                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Citation

yolov5 model https://github.com/ultralytics/yolov5

lane detection www.bilibili.com/video/BV1qk4y1r7jw