基于深度学习的交通信号灯检测系统.zip

# 基于深度学习的交通信号灯检测系统 This app detects cars/buses in a live traffic at a phenomenal **50 frames/sec with HD resolution (1920x1080)** using deep learning network [Yolo-V2](https://pjreddie.com/darknet/yolov2). The model used in the app is optimized for inferencing performnce on AMD-GPUs using [MIVisionX toolkit](https://gpuopen-professionalcompute-libraries.github.io/MIVisionX/). [](https://youtu.be/YASOovwds_A) ## Features 1. Vehicle detection with bounding box 1. Vehicle direction ((upward, downward) detection 1. Vehicle speed estimation 1. Vehicle type: bus/car. ## How to Run ### Use Model <img src="media/speed_detection_user_interface.jpg" width=600> ### Demo App starts the demo, if no other option is provided. Demo uses a video stored in the [media/](./media) dir. ``` % ./main.py ('Loaded', 'yoloOpenVX') OK: loaded 22 kernels from libvx_nn.so OK: OpenVX using GPU device#0 (gfx900) [OpenCL 1.2 ] [SvmCaps 0 1] OK: annCreateInference: successful Processed a total of 102 frames OK: OpenCL buffer usage: 87771380, 46/46 % ``` Here is the [link to YouTube video](https://youtu.be/YASOovwds_A) detecting cars, bounding boxes, car speed, and confidence scores. ### Other Examples **recorded video** > 1. ./main.py --video <path-to-video>/vid.mp4 **traffic cam ip** > 2. ./main.py --cam_ip 'http://166.149.104.112:8082/snap.jpg' ## Installation ### Prerequisites 1. GPU: Radeon Instinct or Vega Family of Products with [ROCm](https://rocm.github.io/ROCmInstall.html) and OpenCL development kit 1. [Install AMD's MIVisionX toolkit](https://gpuopen-professionalcompute-libraries.github.io/MIVisionX/) : AMD's MIVisionX toolkit is a comprehensive computer vision and machine intelligence libraries, utilities 1. [CMake](http://cmake.org/download/), [Caffe](http://caffe.berkeleyvision.org/installation.html) 1. [Google's Protobuf](https://github.com/google/protobuf) ### Steps ``` % git clone https://github.com/srohit0/trafficVision ``` **_1. Model Conversion_** This steps downloads yolov2-tiny for voc dataset and converts to MIVision's openVX model. ``` % cd trafficVision/model % bash ./prepareModel.sh ``` More details on the pre-requisite (like [caffe](http://caffe.berkeleyvision.org/installation.html)) of the model conversion in the [models/](./models) dir. **_2. MIVision Model Compilation_** ``` % cd trafficVision % make ``` **_3. Test App_** ``` % cd trafficVision % make test ``` It'll display detection all videos in media/ dir. ## Design This section is a guide for developers, who would like to port vision and object detections models to AMD's Radeon GPUs from other frameworks including [tensorflow](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md), [caffe](http://caffe.berkeleyvision.org/model_zoo.html) or [pytorch](https://pytorch.org/). ### High Level Design <img src="media/speed_detection_top_level_arch.jpg" width="600"> ### Lower Level Modules These lower level modules can be found as python modules (files) or packages (directories) in this repository. <img src="media/speed_detection_modules.jpg" width="600"> ## Development ### Model Conversion Follow model conversion process similar to the one described below. <img src="media/speed_detection_model_conversion.jpg" width=680> ### Infrastructure Make sure you've infrastructure pre-requisites installed before you start porting neural network model for inferencing. <img src="media/speed_detection_infrastructure.jpg" width=480> ## Developed and Tested on 1. Hardware 1. AMD Ryzen Threadripper 1900X 8-Core Processor 1. Accelerator = Radeon Instinct™ MI25 Accelerator 1. Software 1. Ubuntu 16.04 LTS OS 1. Python 2.7 1. MIVisionX 1.7.0 1. AMD OpenVX 0.9.9 1. GCC 5.4 ## Credit * MIVisionX Team ## References 1. [yoloV2 paper](https://arxiv.org/pdf/1612.08242.pdf) 1. [Tiny Yolo aka Darknet reference network](https://pjreddie.com/darknet/imagenet/#reference) 1. [MiVisionX Setup](https://github.com/kiritigowda/MIVisionX-setup) 1. [AMD OpenVX](https://gpuopen.com/compute-product/amd-openvx/) 1. [Optimization with OpenVX Graphs](http://openaccess.thecvf.com/content_cvpr_workshops_2014/W17/papers/Rainey_Addressing_System-Level_Optimization_2014_CVPR_paper.pdf) 1. [Measuring Traffic Speed With Deep Learning Object Detection](https://medium.com/datadriveninvestor/measuring-traffic-speed-with-deep-learning-object-detection-efc0bb9a3c57)