车牌检测（分割+识别）

2026/8/16 11:16:29 拓冰建站浏览量

简介
本篇文章介绍如何实现，车辆车牌的检测分割，以及车牌的识别

实现步骤

基于YOLOv5模型，训练车牌检测器，训练得到权重”yolov5m.pt“；
基于MobileNetV2模型，训练车牌号识别器，训练得到权重”；crnn_rnet.pt“；
（上述权重不分享，请需要的uu自行训练或找资源
输入待检测图片，车牌检测器输出车牌截图crop；
将车牌crop输入车牌号识别器(类似encoder)和车牌颜色识别器，分别输出预测；
对车牌号预测结果进行文本译码，得到最终预测的车牌号。

补充：更为完善的流程是在进行车牌识别前，先进行车牌对齐的前处理，感兴趣的uu自行搜索，或者问我
其实如果输入的车牌图像，只含有足够清晰、完整的车牌，那么只使用车牌号识别器也足够了，但由于甲方那边拿来检测的图像含有嘈杂的背景，所以只能先进行车牌的检测分割，再识别。（后面给关键代码的时候也会由简至难

全局部分

import os
import numpy as np
import cv2
import random
import torch
import jsonfrom torchvision import transforms
# 下面是在导入车牌号识别器的backbone哦，没有得导会报错的
from crnn.crnn_rnet import CRNN_RNETos.environ['CUDA_VISIBLE_DEVICES'] = '0,1'# 初始化车牌号识别器
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pt = "./crnn_rnet.pt"
config = torch.load(pt)["config"]
backbone = CRNN_RNET(nChannel=3,nHeight=config.height,nClass=config.maxLabel,nHidden=config.rnet.hidden,
).to(device)
backbone.load_state_dict(torch.load(pt)["data"])
backbone.eval()

（下面直接给关键代码，不理解请评论区

文本译码（被获取车牌信息函数调用）

def text_decode(config, data, confs, length):if length.numel() == 1:length = length[0]assert (data.numel() == length), "text with length: {} does not match declared length: {}".format(data.numel(), length)char_list = []confs_list = []for i in range(length):if data[i] != 0 and (not (i > 0 and data[i - 1] == data[i])):if config.text[data[i] - 1] != " ":char_list.append(config.text[data[i] - 1])confs_list.append(confs[i].item())confs_list = [i * 100 for i in confs_list]confs_list = [round(x, 1) for x in confs_list]plateCharReliability = ""for i in range(0, len(char_list)):if i != len(char_list) - 1:plateCharReliability += f"{char_list[i]}-{confs_list[i]}%, "else:plateCharReliability += f"{char_list[i]}-{confs_list[i]}%"return "".join(char_list), plateCharReliabilityelse:assert (data.numel() == length.sum()), "texts with length: {} does not match declared length: {}".format(data.numel(), length.sum())texts = []index = 0for i in range(length.numel()):l = length[i]texts.append(config.text(data[index : index + l], torch.IntTensor([l])))index += lreturn texts

获取车牌信息

transform = transforms.Compose([transforms.ToTensor()])def get_plate_info(plate_path):im = cv2.imread(plate_path)# plate_color_recognition是车牌颜色识别函数，因为用的是直接统计像素值，简单粗暴的方法（并且代码又臭又长），不好意思在这里展示了，但如果有uu需要也可以找我要# vehicleColor = plate_color_recognition(im)im = cv2.resize(im, (config.width, config.height))im = transform(im)im = im[None].to(device)with torch.no_grad():preds = backbone(im)confs, preds = preds.max(2)preds = preds.transpose(1, 0).contiguous().view(-1)confs = confs.transpose(1, 0).contiguous().view(-1)preds_size = torch.IntTensor([preds.size(0)] * im.size(0))sim_preds, plateCharReliability = text_decode(config, preds.data, confs.data, preds_size.data)# 下面这句单纯为了打印出来比较好看sim_preds = sim_preds.replace(" ", "")# 打印车牌号，以及每位号牌号码可信度# 例：闽D123456 闽-100.0%, D-100.0%, 1-100.0%, 2-100.0%, 3-100.0%, 4-100.0%, 5-100.0%, 6-100.0%print(sim_preds, plateCharReliability)# return sim_preds, plateCharReliability

车牌检测器（YOLOv5）

其实和ultralytics给出的predict.py代码一样(run(), parse_opt(), main())
YOLOv5项目地址: https://github.com/ultralytics/yolov5
predict.py: https://github.com/ultralytics/yolov5/blob/master/segment/predict.py
我对源码进行了修改，破破烂烂的，大家就当看看得了，参考还是要看源码


@smart_inference_mode()
def run(weights=ROOT / "yolov5m.pt",source=ROOT / "data/images",  # file/dir/URL/glob/screen/0(webcam)data=ROOT / "data/coco128.yaml",  # dataset.yaml pathimgsz=(640, 640),  # inference size (height, width)conf_thres=0.25,  # confidence thresholdiou_thres=0.45,  # NMS IOU thresholdmax_det=1000,  # maximum detections per imagedevice="",  # cuda device, i.e. 0 or 0,1,2,3 or cpuview_img=False,  # show resultssave_txt=False,  # save results to *.txtsave_csv=False,  # save results in CSV formatsave_conf=False,  # save confidences in --save-txt labelssave_crop=False,  # save cropped prediction boxesnosave=False,  # do not save images/videosclasses=[0],  # 只有车牌类，传给yolov5magnostic_nms=False,  # class-agnostic NMSaugment=False,  # augmented inferencevisualize=False,  # visualize featuresupdate=False,  # update all modelsproject=ROOT / "../results/detect",  # save results to project/namename="result",  # save results to project/nameexist_ok=False,  # existing project/name ok, do not incrementline_thickness=3,  # bounding box thickness (pixels)hide_labels=False,  # hide labelshide_conf=False,  # hide confidenceshalf=False,  # use FP16 half-precision inferencednn=False,  # use OpenCV DNN for ONNX inferencevid_stride=1,  # video frame-rate stride
):source = str(source)save_img = not nosave and not source.endswith(".txt")  # save inference imagesis_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))webcam = (source.isnumeric() or source.endswith(".streams") or (is_url and not is_file))screenshot = source.lower().startswith("screen")if is_url and is_file:source = check_file(source)  # download# Directoriessave_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # Load modeldevice = select_device(device)# Load plate detector modelmodel = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)stride, names, pt = model.stride, model.names, model.ptimgsz = check_img_size(imgsz, s=stride)  # check image size# Dataloaderbs = 1  # batch_sizeif webcam:view_img = check_imshow(warn=True)dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)bs = len(dataset)elif screenshot:dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)else:dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)vid_path, vid_writer = [None] * bs, [None] * bsplate_path = ""# Run inferencemodel.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmupseen, windows, dt = 0, [], (Profile(), Profile(), Profile())for path, im, im0s, vid_cap, s in dataset:# 以下遍历每一帧提取的内容with dt[0]:im = torch.from_numpy(im).to(model.device)im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32im /= 255  # 0 - 255 to 0.0 - 1.0if len(im.shape) == 3:im = im[None]  # expand for batch dim# Inferencewith dt[1]:visualize = (increment_path(save_dir / Path(path).stem, mkdir=True)if visualizeelse False)pred = model(im, augment=augment, visualize=visualize)# NMSwith dt[2]:pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)# Process predictions# pred包含了这一帧里所有检测到的目标，但是该循环只会进行一次，该循环还没有进入到对每个检测框的遍历for i, det in enumerate(pred):  # per imageseen += 1if webcam:  # batch_size >= 1p, im0, frame = path[i], im0s[i].copy(), dataset.count# s += f'{i}: 'else:p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)p = Path(p)  # to Pathsave_path = str(save_dir / p.name)  # im.jpgtxt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}")  # im.txt# s += '%gx%g ' % im.shape[2:]  # print stringgn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhimc = im0.copy() if save_crop else im0  # for save_cropannotator = Annotator(im0, line_width=line_thickness, example=str(names))if len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()# Print resultsfor c in det[:, -1].unique():n = (det[:, -1] == c).sum()  # detections per classif c == 0:s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string# Write resultsfor *xyxy, conf, cls in reversed(det):if save_img or save_crop or view_img:  # Add bbox to imagec = int(cls)  # integer class,c=0时为车牌if c == 0:plate_path = save_one_box(xyxy,imc,file=save_dir/ "crops"/ names[c]/ f"{p.stem}.jpg",BGR=True,)else:print("no plate")return 0if save_txt or save_img:s = (f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}"if save_txtelse "")# LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")if update:strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)return plate_pathdef parse_opt(source):parser = argparse.ArgumentParser()parser.add_argument("--weights",nargs="+",type=str,default=ROOT / "yolov5m.pt",help="model path or triton URL",)parser.add_argument("--source",type=str,default=ROOT / "data/images",help="file/dir/URL/glob/screen/0(webcam)",)parser.add_argument("--data",type=str,default=ROOT / "data/coco128.yaml",help="(optional) dataset.yaml path",)parser.add_argument("--imgsz","--img","--img-size",nargs="+",type=int,default=[640],help="inference size h,w",)parser.add_argument("--conf-thres", type=float, default=0.3, help="confidence threshold")parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold")parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image")parser.add_argument("--device", default="0", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")parser.add_argument("--view-img", action="store_true", help="show results")parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")parser.add_argument("--save-csv", action="store_true", help="save results in CSV format")parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes")parser.add_argument("--nosave", action="store_true", help="do not save images/videos")parser.add_argument("--classes",nargs="+",type=int,default=[0],help="filter by class: --classes 0, or --classes 0 2 3",)parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS")parser.add_argument("--augment", action="store_true", help="augmented inference")parser.add_argument("--visualize", action="store_true", help="visualize features")parser.add_argument("--update", action="store_true", help="update all models")parser.add_argument("--project",default=ROOT / "../results/interface4",help="save results to project/name",)parser.add_argument("--name", default="exp", help="save results to project/name")parser.add_argument("--exist-ok",action="store_true",help="existing project/name ok, do not increment",)parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)")parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels")parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences")parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")parser.add_argument("--vid-stride", type=int, default=30, help="video frame-rate stride")opt = parser.parse_args()opt.source = sourceopt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expandprint_args(vars(opt))return opt

小Trick

先检测分割后识别有一个避免不了的bug，如果检测车牌的检测器不够准确会直接影响识别的准确度
例如：1. 分割出来的车牌不完整；2. 车牌占比过大，没检测到车牌
（针对两个bug的代码，我删了，但我可以描述我处理的方法）

分割不完整，一般是车牌高准确，宽不足，一般车牌长×宽=140×440(mm)比例约为3.14，可以根据比例调整分割出来的车牌crop
因为占比过大导致检测不出车牌，那就跳过车牌检测器，直接使用车牌识别器

建站实战干货

车牌检测（分割+识别）

实现步骤

文本译码（被获取车牌信息函数调用）

获取车牌信息

车牌检测器（YOLOv5）

小Trick