基于yolov5+LPRNet 车牌定位识别源码+模型+项目运行说明.zip

# 车牌识别 ## 原理 - YOLOv5：车牌定位 - LPRNet：车牌识别 ## 使用方法 | 参数 | 说明 | 默认 | | ---- | ---- | --- | | --source | 照片文件夹路径 | ./inference/images/ | | --output | 保存结果路径 | ./inference/output | | --conf-thres | object confidence threshold | 0.4 | | --iou-thres | IOU threshold for NMS | 0.5 | | --view-img | 显示结果 | false | | --save-txt | 保存结果为txt | false | | --augment | augmented inference | false | 默认 ```bash python ./main.py ``` 保存识别结果为txt ```bash python ./main.py --save-txt ``` 显示识别结果 ```bash python ./main.py --view-img ``` 自定义输入与输出 ```bash python ./main.py --source ../car --output ./output ``` --- `识别效果`   ## 关于YOLOv5的训练 [数据集参数](./yolov5/data/plat.yaml) ```yaml train: /data/CCPD2020/images/train/ # 16551 images val: /data/CCPD2020/images/val/ # 4952 images # number of classes nc: 1 # class names names: [ 'plate'] ``` model: `yolov5s ` [train.py的超参数](./yolov5/train.py) ```python parser = argparse.ArgumentParser() parser.add_argument('--weights', type=str, default='./weights/yolov5s.pt', help='initial weights path') parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path') parser.add_argument('--data', type=str, default='data/plat.yaml', help='data.yaml path') parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path') parser.add_argument('--epochs', type=int, default=300) parser.add_argument('--batch-size', type=int, default=8, help='total batch size for all GPUs') parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes') parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--notest', action='store_true', help='only test final epoch') parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check') parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer') parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') parser.add_argument('--workers', type=int, default=4, help='maximum number of dataloader workers') parser.add_argument('--project', default='runs/train', help='save to project/name') parser.add_argument('--entity', default=None, help='W&B entity') parser.add_argument('--name', default='exp', help='save to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--quad', action='store_true', help='quad dataloader') parser.add_argument('--linear-lr', action='store_true', help='linear LR') parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table') parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B') parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch') parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used') ``` ## LPRNet - 训练教程请看[tutorial.ipynb](./LPRNet/tutorial.ipynb)，数据集使用`chinese_license_plate_generator`生成的，或者使用CCPD2020经YOLOv5剪切出来的车牌。 - 使用预训练好的模型去再训练，记得要把dropout关闭，可参考[demo.ipynb](./LPRNet/demo.ipynb) ## 引用的代码来源 - [yolov5](https://github.com/ultralytics/yolov5) - [LPRNet](https://github.com/sirius-ai/LPRNet_Pytorch) - [chinese_license_plate_generator](https://gitee.com/leijd/chinese_license_plate_generator)