# Image-based-Object-Detection-System-for-Self-driving-Cars-Application **Repository Path**: TimVerion/Image-based-Object-Detection-System-for-Self-driving-Cars-Application ## Basic Information - **Project Name**: Image-based-Object-Detection-System-for-Self-driving-Cars-Application - **Description**: Image-based Object Detection System for Self-driving Cars Application - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-01-21 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Image-based-Object-Detection-System-for-Self-driving-Cars-Application The Goal of this project: Detect and track 4 different objects includes vehicle, pedestrian, cyclist and traffic lights (labeled as 1, 2, 3, 20).   And I have done some things below:   1.Base on Deep learning (Mxnet) to implement object detection and tracking system on self-driving car system       2.Based on given dataset and Yolo algorithm to construct special neural network model and update a new loss function       3.Utilize GPU for training and tune parameters to converge and optimize the result         4.Optimize feedforward inference network and realize object detection and tracking in real time on camera     (In the future) Move the system to robots for avoiding obstacle See demo below or see result in jupyter notebook's result Yolo algorithm. There are 2 verson for it. [v1](https://arxiv.org/pdf/1506.02640.pdf), [v2](https://arxiv.org/pdf/1612.08242.pdf). [Interperation video of my algorithm and codes](https://drive.google.com/file/d/1-v0i_KixemGHBmBk2lMOLATMamX6n-JC/view?usp=sharing) # Dependencies Python,Mxnet 1.0, cuda8.0, tensorboardX,cudnn,opencv,GPU:nvidia 1070T # Yolo-v1   # Algorithm & Model Structure   Transform detection and classification problems in a regression problem ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/0.jpg) Data and Model https://drive.google.com/drive/u/0/folders/0BwXw1vJFiBDaZ1IwNjlEd0RZMFU For asking training Dataset and testing Dataset, you could send me email. In original dataset: 10k images, 593 images do not have bbox,53910 bbox totally vehicle : 0.84; Pedestrian : 0.07; Cyclist : 0.06; Traffic light : 0.03 # To run the code:   !!!First you need to change the code's path and make it suitable in your Pc  Download data , model and label cd to the path of "new_data" and run it to generate 50k new data mkdir and cd to the path of "DATA_rec/" and the json you need is in new_data run "python data_util/py" for data preparaion with train and val recfile cd to the src root path and run "pyton run_train.py" # For the test:   Please take a look of wild_test.ipynb and demo_test.ipynb in src first. And then run test.py which could output a json file for results and draw the bbox in image For real time predict, to run "pythonw real_time_object_detection" on Mac/ "python real_time_object_detection" # Result After about 24 hour's training(350 epoch),accuracy is about 0.99,precision is 0.85, recall is 0.98,h_diff is 1.3, w_diff is 1.23   The result is shown below: ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/1.png) ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/2.png) ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/3.png)   ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/4.png)   ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/5.png)   ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/6.png)   ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/7.png)   ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/8.png)   ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/9.png)   Demo for test data: ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/t1.jpg) ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/t2.jpg)   ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/t3.jpg)   ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/t4.jpg)   ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/t5.jpg)   ![image](https://github.com/YunchuZhang/Image-based-Object-Detection-System-for-Self-driving-Cars-Application/blob/master/readme/t6.jpg) Real-time test: 1.video data test: https://drive.google.com/open?id=1a9H8viB03dgJFk3aSzO0xqn8tFwSbmVm 2.real-time test on road:https://drive.google.com/file/d/1_T68yN0gDBtviDOgbKAwND5fq5EzwpI1/view?usp=sharing # Refer: http://blog.topspeedsnail.com/archives/2068 https://www.pyimagesearch.com/2017/09/18/real-time-object-detection-with-deep-learning-and-opencv/ # Yolo-v2 Is writting and updating # Result Demo for test data: Real-time test: