Parking_Management:停车管理系统

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  • 2022-05-15 09:56
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停车管理 目录 关于 该项目旨在有效处理整个停车场的车辆停车服务。 车辆使用其车牌信息进行唯一标识,并在各个摄像头位置进行跟踪。 监控停放的汽车的位置,以方便参考和使用。 还计算出停车位的空位,以管理停车场的停车容量。 重点已放在使系统强大以应对低光照环境和不受限制的摄像机角度方面。 特征 车辆检测模型基于使用Darknet框架执行的YOLOv4架构 使用用于车牌检测的自定义Keras模型和用于执行车牌字符OCR的基于YOLO的检测器来处理ALPR 所用包装 TensorFlow-1.15.4(CPU)/ TensorFlow 1.13.1(GPU-Colab) 凯拉斯-2.2.4 FastAPI OpenCV 盗用者 巢式异步 彭格罗克 阿斯吉夫 设置 要求 Python 3.6+ 安装 要在本地系统上安装必需的软件包: pip install -r requirements.tx
Parking_Management-main.zip
内容介绍
# Yolo v4, v3 and v2 for Windows and Linux ## (neural networks for object detection) Paper YOLO v4: https://arxiv.org/abs/2004.10934 Paper Scaled YOLO v4: https://arxiv.org/abs/2011.08036 use to reproduce results: [ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) More details in articles on medium: * [Scaled_YOLOv4](https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8) * [YOLOv4](https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7) Manual: https://github.com/AlexeyAB/darknet/wiki Discussion: - [Reddit](https://www.reddit.com/r/MachineLearning/comments/gydxzd/p_yolov4_the_most_accurate_realtime_neural/) - [Google-groups](https://groups.google.com/forum/#!forum/darknet) - [Discord](https://discord.gg/zSq8rtW) About Darknet framework: http://pjreddie.com/darknet/ [![Darknet Continuous Integration](https://github.com/AlexeyAB/darknet/workflows/Darknet%20Continuous%20Integration/badge.svg)](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22) [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet) [![TravisCI](https://travis-ci.org/AlexeyAB/darknet.svg?branch=master)](https://travis-ci.org/AlexeyAB/darknet) [![Contributors](https://img.shields.io/github/contributors/AlexeyAB/Darknet.svg)](https://github.com/AlexeyAB/darknet/graphs/contributors) [![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE) [![DOI](https://zenodo.org/badge/75388965.svg)](https://zenodo.org/badge/latestdoi/75388965) [![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2004.10934-B31B1B.svg)](https://arxiv.org/abs/2004.10934) [![colab](https://user-images.githubusercontent.com/4096485/86174089-b2709f80-bb29-11ea-9faf-3d8dc668a1a5.png)](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) [![colab](https://user-images.githubusercontent.com/4096485/86174097-b56b9000-bb29-11ea-9240-c17f6bacfc34.png)](https://colab.research.google.com/drive/1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg) * [YOLOv4 model zoo](https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo) * [Requirements (and how to install dependecies)](#requirements) * [Pre-trained models](#pre-trained-models) * [FAQ - frequently asked questions](https://github.com/AlexeyAB/darknet/wiki/FAQ---frequently-asked-questions) * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) * [Yolo v4 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn, TVM,...)](#yolo-v4-in-other-frameworks) * [Datasets](#datasets) 0. [Improvements in this repository](#improvements-in-this-repository) 1. [How to use](#how-to-use-on-the-command-line) 2. How to compile on Linux * [Using cmake](#how-to-compile-on-linux-using-cmake) * [Using make](#how-to-compile-on-linux-using-make) 3. How to compile on Windows * [Using cmake](#how-to-compile-on-windows-using-cmake) * [Using vcpkg](#how-to-compile-on-windows-using-vcpkg) * [Legacy way](#how-to-compile-on-windows-legacy-way) 4. [Training and Evaluation of speed and accuracy on MS COCO](https://github.com/AlexeyAB/darknet/wiki#training-and-evaluation-of-speed-and-accuracy-on-ms-coco) 5. [How to train with multi-GPU:](#how-to-train-with-multi-gpu) 6. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) 7. [How to train tiny-yolo (to detect your custom objects)](#how-to-train-tiny-yolo-to-detect-your-custom-objects) 8. [When should I stop training](#when-should-i-stop-training) 9. [How to improve object detection](#how-to-improve-object-detection) 10. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) 11. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries) ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) ![scaled_yolov4](https://user-images.githubusercontent.com/4096485/101356322-f1f5a180-38a8-11eb-9907-4fe4f188d887.png) AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036 ---- ![modern_gpus](https://user-images.githubusercontent.com/4096485/82835867-f1c62380-9ecd-11ea-9134-1598ed2abc4b.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 tkDNN-TensorRT accelerates YOLOv4 **~2x** times for batch=1 and **3x-4x** times for batch=4. * tkDNN: https://github.com/ceccocats/tkDNN * OpenCV: https://gist.github.com/YashasSamaga/48bdb167303e10f4d07b754888ddbdcf #### GeForce RTX 2080 Ti: | Network Size | Darknet, FPS (avg)| tkDNN TensorRT FP32, FPS | tkDNN TensorRT FP16, FPS | OpenCV FP16, FPS | tkDNN TensorRT FP16 batch=4, FPS | OpenCV FP16 batch=4, FPS | tkDNN Speedup | |:-----:|:--------:|--------:|--------:|--------:|--------:|--------:|------:| |320 | 100 | 116 | **202** | 183 | 423 | **430** | **4.3x** | |416 | 82 | 103 | **162** | 159 | 284 | **294** | **3.6x** | |512 | 69 | 91 | 134 | **138** | 206 | **216** | **3.1x** | |608 | 53 | 62 | 103 | **115**| 150 | **150** | **2.8x** | |Tiny 416 | 443 | 609 | **790** | 773 | **1774** | 1353 | **3.5x** | |Tiny 416 CPU Core i7 7700HQ | 3.4 | - | - | 42 | - | 39 | **12x** | * Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80283279-0e303e00-871f-11ea-814c-870967d77fd1.png) * Yolo v4 tiny comparison: [tiny_fps](https://user-images.githubusercontent.com/4096485/85734112-6e366700-b705-11ea-95d1-fcba0de76d72.png) * CSPNet: [paper](https://arxiv.org/abs/1911.11929) and [map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png) comparison: https://github.com/WongKinYiu/CrossStagePartialNetworks * Yolo v3 on MS COCO: [Speed / Accuracy (mAP@0.5) chart](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg) * Yolo v3 on MS COCO (Yolo v3 vs RetinaNet) - Figure 3: https://arxiv.org/pdf/1804.02767v1.pdf * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg #### Youtube video of results | [![Yolo v4](https://user-images.githubusercontent.com/4096485/101360000-1a33cf00-38ae-11eb-9e5e-b29c5fb0afbe.png)](https://youtu.be/1_SiUOYUoOI "Yolo v4") | [![Scaled Yolo v4](https://user-images.githubusercontent.com/4096485/101359389-43a02b00-38ad-11eb-866c-f813e96bf61a.png)](https://youtu.be/YDFf-TqJOFE "Scaled Yolo v4") | |---|---| Others: https://www.youtube.com/user/pjreddie/videos #### How to evaluate AP of YOLOv4 on the MS COCO evaluation server 1. Download and unzip test-dev2017 dataset from MS COCO server: http://images.cocodataset.org/zips/test2017.zip 2. Download list of images for Detection taks and replace the paths with yours: https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt 3. Download `yolov4.weights` file 245 MB: [yolov4.weights](https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights) (Google-drive mirror [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) ) 4. Content of the file `cfg/coco.data` should be ```ini classes= 80 train = <replace with your path>/trainvalno5k.txt valid = <replace with your path>/testdev2017.txt names = data/coco.names backup = backup eval=coco ``` 5. Create `/results/` folder near with `./darknet` executable file 6. Run validation: `./darknet detector valid cfg/coco.data cfg/yolov4.cfg yolov4.weights` 7. Rename the file `/results/coco_results.json` to `detections_test-de
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