毕设开源 深度学习 YOLO 实现车牌识别算法

文章目录

• 0 前言
• 1 课题介绍
• 2 算法简介
  • 2.1网络架构
• 3 数据准备
• 4 模型训练
• 5 实现效果
  • 5.1 图片识别效果
  • 5.2视频识别效果
• 6 部分关键代码

0 前言

🔥这两年开始毕业设计和毕业答辩的要求和难度不断提升，传统的毕设题目缺少创新和亮点，往往达不到毕业答辩的要求，这两年不断有学弟学妹告诉学长自己做的项目系统达不到老师的要求。并且很难找到完整的毕设参考学习资料。

为了大家能够顺利以及最少的精力通过毕设，学长分享优质毕业设计项目提供大家参考学习，今天要分享的是

🚩 基于yolov5的深度学习车牌识别系统实现

🥇学长这里给一个题目综合评分(每项满分5分)

• 难度系数：4分
• 工作量：4分
• 创新点：3分

🧿 选题指导, 项目分享：见文末

1 课题介绍

智能车牌识别是现代智能交通系统的重要组成部分， 广泛应用于高速公路、停车场、路口等场景。随着大数 据、人工智能的不断发展，智能车牌识别在数据处理、自 适应学习以及特殊场景训练等方面都有较大程度提升，具 有更强的容错性和鲁棒性。通过车牌号码的自动识别与跟 踪，能有效降低车辆自动化管理的成本，规范车辆不规范 行为，为社会稳定与居民便捷生活提供坚实保障。

2 算法简介

YOLOv5是一种单阶段目标检测算法，该算法在YOLOv4的基础上添加了一些新的改进思路，使其速度与精度都得到了极大的性能提升。主要的改进思路如下所示：

输入端：在模型训练阶段，提出了一些改进思路，主要包括Mosaic数据增强、自适应锚框计算、自适应图片缩放；
基准网络：融合其它检测算法中的一些新思路，主要包括：Focus结构与CSP结构；
Neck网络：目标检测网络在BackBone与最后的Head输出层之间往往会插入一些层，Yolov5中添加了FPN+PAN结构；
Head输出层：输出层的锚框机制与YOLOv4相同，主要改进的是训练时的损失函数GIOU_Loss，以及预测框筛选的DIOU_nms。

2.1网络架构

上图展示了YOLOv5目标检测算法的整体框图。对于一个目标检测算法而言，我们通常可以将其划分为4个通用的模块，具体包括：输入端、基准网络、Neck网络与Head输出端，对应于上图中的4个红色模块。YOLOv5算法具有4个版本，具体包括：YOLOv5s、YOLOv5m、YOLOv5l、YOLOv5x四种，本文重点讲解YOLOv5s，其它的版本都在该版本的基础上对网络进行加深与加宽。

• 输入端-输入端表示输入的图片。该网络的输入图像大小为608*608，该阶段通常包含一个图像预处理阶段，即将输入图像缩放到网络的输入大小，并进行归一化等操作。在网络训练阶段，YOLOv5使用Mosaic数据增强操作提升模型的训练速度和网络的精度；并提出了一种自适应锚框计算与自适应图片缩放方法。
• 基准网络-基准网络通常是一些性能优异的分类器种的网络，该模块用来提取一些通用的特征表示。YOLOv5中不仅使用了CSPDarknet53结构，而且使用了Focus结构作为基准网络。
• Neck网络-Neck网络通常位于基准网络和头网络的中间位置，利用它可以进一步提升特征的多样性及鲁棒性。虽然YOLOv5同样用到了SPP模块、FPN+PAN模块，但是实现的细节有些不同。
• Head输出端-Head用来完成目标检测结果的输出。针对不同的检测算法，输出端的分支个数不尽相同，通常包含一个分类分支和一个回归分支。YOLOv4利用GIOU_Loss来代替Smooth L1 Loss函数，从而进一步提升算法的检测精度。

3 数据准备

大家可选用公开的车牌识别数据集。如标注好的 CCPD 数据集， CCPD 数据集一共包含超多 25 万张图片，每种图片大小 720x1160x3，选取部分 CCPD 数据集作为本设计中的车牌检 测与识别的数据集，总共包含 9 项。

也可自己收集车牌图片标注数据集，数据标注这里推荐的软件是labelimg，通过pip指令即可安装。具体使用可上网查看教程。

4 模型训练

修改train.py中的weights、cfg、data、epochs、batch_size、imgsz、device、workers等参数

训练代码成功执行之后会在命令行中输出下列信息，接下来就是安心等待模型训练结束即可。

5 实现效果

来看看我们要实现的效果，我们将会通过数据来训练一个车牌识别的模型，并用pyqt5进行封装，实现图片车牌识别、视频车牌识别和摄像头实时车牌识别的功能。

if __name__ =='__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+',type=str, default='./weights/last.pt',help='model.pt path(s)') parser.add_argument('--source',type=str, default='./inference/images',help='source')# file/folder, 0 for webcam parser.add_argument('--output',type=str, default='inference/output',help='output folder')# output folder parser.add_argument('--img-size',type=int, default=640,help='inference size (pixels)') parser.add_argument('--conf-thres',type=float, default=0.8,help='object confidence threshold') parser.add_argument('--iou-thres',type=float, default=0.5,help='IOU threshold for NMS') parser.add_argument('--device', default='',help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--view-img', action='store_true',help='display results',default=True) parser.add_argument('--save-txt', action='store_true',help='save results to *.txt') parser.add_argument('--classes', nargs='+',type=int,help='filter by class') 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('--update', action='store_true',help='update all models') opt = parser.parse_args()print(opt)with torch.no_grad():if opt.update:# update all models (to fix SourceChangeWarning)for opt.weights in['yolov5s.pt','yolov5m.pt','yolov5l.pt','yolov5x.pt','yolov3-spp.pt']: detect() create_pretrained(opt.weights, opt.weights)else:

5.1 图片识别效果

5.2视频识别效果

6 部分关键代码

篇幅有限，仅展示部分代码

classDetect(nn.Module): stride =None# strides computed during build onnx_dynamic =False# ONNX export parameterdef__init__(self, nc=80, anchors=(), ch=(), inplace=True):# detection layersuper().__init__() self.nc = nc # number of classes self.no = nc +5# number of outputs per anchor self.nl =len(anchors)# number of detection layers self.na =len(anchors[0])//2# number of anchors self.grid =[torch.zeros(1)]* self.nl # init grid self.anchor_grid =[torch.zeros(1)]* self.nl # init anchor grid self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl,-1,2))# shape(nl,na,2) self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na,1)for x in ch)# output conv self.inplace = inplace # use in-place ops (e.g. slice assignment)defforward(self, x): z =[]# inference outputfor i inrange(self.nl): x[i]= self.m[i](x[i])# conv bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) x[i]= x[i].view(bs, self.na, self.no, ny, nx).permute(0,1,3,4,2).contiguous()ifnot self.training:# inferenceif self.onnx_dynamic or self.grid[i].shape[2:4]!= x[i].shape[2:4]: self.grid[i], self.anchor_grid[i]= self._make_grid(nx, ny, i) y = x[i].sigmoid()if self.inplace: y[...,0:2]=(y[...,0:2]*2-0.5+ self.grid[i])* self.stride[i]# xy y[...,2:4]=(y[...,2:4]*2)**2* self.anchor_grid[i]# whelse:# for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953 xy =(y[...,0:2]*2-0.5+ self.grid[i])* self.stride[i]# xy wh =(y[...,2:4]*2)**2* self.anchor_grid[i]# wh y = torch.cat((xy, wh, y[...,4:]),-1) z.append(y.view(bs,-1, self.no))return x if self.training else(torch.cat(z,1), x)def_make_grid(self, nx=20, ny=20, i=0): d = self.anchors[i].device if check_version(torch.__version__,'1.10.0'):# torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)], indexing='ij')else: yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)]) grid = torch.stack((xv, yv),2).expand((1, self.na, ny, nx,2)).float() anchor_grid =(self.anchors[i].clone()* self.stride[i]) \ .view((1, self.na,1,1,2)).expand((1, self.na, ny, nx,2)).float()return grid, anchor_grid classModel(nn.Module):def__init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):# model, input channels, number of classessuper().__init__()ifisinstance(cfg,dict): self.yaml = cfg # model dictelse:# is *.yamlimport yaml # for torch hub self.yaml_file = Path(cfg).name withopen(cfg, encoding='ascii', errors='ignore')as f: self.yaml = yaml.safe_load(f)# model dict# Define model ch = self.yaml['ch']= self.yaml.get('ch', ch)# input channelsif nc and nc != self.yaml['nc']: LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") self.yaml['nc']= nc # override yaml valueif anchors: LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}') self.yaml['anchors']=round(anchors)# override yaml value self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])# model, savelist self.names =[str(i)for i inrange(self.yaml['nc'])]# default names self.inplace = self.yaml.get('inplace',True)# Build strides, anchors m = self.model[-1]# Detect()ifisinstance(m, Detect): s =256# 2x min stride m.inplace = self.inplace m.stride = torch.tensor([s / x.shape[-2]for x in self.forward(torch.zeros(1, ch, s, s))])# forward m.anchors /= m.stride.view(-1,1,1) check_anchor_order(m) self.stride = m.stride self._initialize_biases()# only run once# Init weights, biases initialize_weights(self) self.info() LOGGER.info('')defforward(self, x, augment=False, profile=False, visualize=False):if augment:return self._forward_augment(x)# augmented inference, Nonereturn self._forward_once(x, profile, visualize)# single-scale inference, traindef_forward_augment(self, x): img_size = x.shape[-2:]# height, width s =[1,0.83,0.67]# scales f =[None,3,None]# flips (2-ud, 3-lr) y =[]# outputsfor si, fi inzip(s, f): xi = scale_img(x.flip(fi)if fi else x, si, gs=int(self.stride.max())) yi = self._forward_once(xi)[0]# forward# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save yi = self._descale_pred(yi, fi, si, img_size) y.append(yi) y = self._clip_augmented(y)# clip augmented tailsreturn torch.cat(y,1),None# augmented inference, traindef_forward_once(self, x, profile=False, visualize=False): y, dt =[],[]# outputsfor m in self.model:if m.f !=-1:# if not from previous layer x = y[m.f]ifisinstance(m.f,int)else[x if j ==-1else y[j]for j in m.f]# from earlier layersif profile: self._profile_one_layer(m, x, dt) x = m(x)# run y.append(x if m.i in self.save elseNone)# save outputif visualize: feature_visualization(x, m.type, m.i, save_dir=visualize)return x def_descale_pred(self, p, flips, scale, img_size):# de-scale predictions following augmented inference (inverse operation)if self.inplace: p[...,:4]/= scale # de-scaleif flips ==2: p[...,1]= img_size[0]- p[...,1]# de-flip udelif flips ==3: p[...,0]= img_size[1]- p[...,0]# de-flip lrelse: x, y, wh = p[...,0:1]/ scale, p[...,1:2]/ scale, p[...,2:4]/ scale # de-scaleif flips ==2: y = img_size[0]- y # de-flip udelif flips ==3: x = img_size[1]- x # de-flip lr p = torch.cat((x, y, wh, p[...,4:]),-1)return p def_clip_augmented(self, y):# Clip YOLOv5 augmented inference tails nl = self.model[-1].nl # number of detection layers (P3-P5) g =sum(4** x for x inrange(nl))# grid points e =1# exclude layer count i =(y[0].shape[1]// g)*sum(4** x for x inrange(e))# indices y[0]= y[0][:,:-i]# large i =(y[-1].shape[1]// g)*sum(4**(nl -1- x)for x inrange(e))# indices y[-1]= y[-1][:, i:]# smallreturn y def_profile_one_layer(self, m, x, dt): c =isinstance(m, Detect)# is final layer, copy input as inplace fix o = thop.profile(m, inputs=(x.copy()if c else x,), verbose=False)[0]/1E9*2if thop else0# FLOPs t = time_sync()for _ inrange(10): m(x.copy()if c else x) dt.append((time_sync()- t)*100)if m == self.model[0]: LOGGER.info(f"{'time (ms)':>10s}{'GFLOPs':>10s}{'params':>10s}{'module'}") LOGGER.info(f'{dt[-1]:10.2f}{o:10.2f}{m.np:10.0f}{m.type}')if c: LOGGER.info(f"{sum(dt):10.2f}{'-':>10s}{'-':>10s} Total")def_initialize_biases(self, cf=None):# initialize biases into Detect(), cf is class frequency# https://arxiv.org/abs/1708.02002 section 3.3# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. m = self.model[-1]# Detect() modulefor mi, s inzip(m.m, m.stride):# from b = mi.bias.view(m.na,-1)# conv.bias(255) to (3,85) b.data[:,4]+= math.log(8/(640/ s)**2)# obj (8 objects per 640 image) b.data[:,5:]+= math.log(0.6/(m.nc -0.999999))if cf isNoneelse torch.log(cf / cf.sum())# cls mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)def_print_biases(self): m = self.model[-1]# Detect() modulefor mi in m.m:# from b = mi.bias.detach().view(m.na,-1).T # conv.bias(255) to (3,85) LOGGER.info(('%6g Conv2d.bias:'+'%10.3g'*6)%(mi.weight.shape[1],*b[:5].mean(1).tolist(), b[5:].mean()))# def _print_weights(self):# for m in self.model.modules():# if type(m) is Bottleneck:# LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weightsdeffuse(self):# fuse model Conv2d() + BatchNorm2d() layers LOGGER.info('Fusing layers... ')for m in self.model.modules():ifisinstance(m,(Conv, DWConv))andhasattr(m,'bn'): m.conv = fuse_conv_and_bn(m.conv, m.bn)# update convdelattr(m,'bn')# remove batchnorm m.forward = m.forward_fuse # update forward self.info()return self defautoshape(self):# add AutoShape module LOGGER.info('Adding AutoShape... ') m = AutoShape(self)# wrap model copy_attr(m, self, include=('yaml','nc','hyp','names','stride'), exclude=())# copy attributesreturn m definfo(self, verbose=False, img_size=640):# print model information model_info(self, verbose, img_size)def_apply(self, fn):# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers self =super()._apply(fn) m = self.model[-1]# Detect()ifisinstance(m, Detect): m.stride = fn(m.stride) m.grid =list(map(fn, m.grid))ifisinstance(m.anchor_grid,list): m.anchor_grid =list(map(fn, m.anchor_grid))return self defparse_model(d, ch):# model_dict, input_channels(3) LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}{'module':<40}{'arguments':<30}") anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] na =(len(anchors[0])//2)ifisinstance(anchors,list)else anchors # number of anchors no = na *(nc +5)# number of outputs = anchors * (classes + 5) layers, save, c2 =[],[], ch[-1]# layers, savelist, ch outfor i,(f, n, m, args)inenumerate(d['backbone']+ d['head']):# from, number, module, args m =eval(m)ifisinstance(m,str)else m # eval stringsfor j, a inenumerate(args):try: args[j]=eval(a)ifisinstance(a,str)else a # eval stringsexcept NameError:pass n = n_ =max(round(n * gd),1)if n >1else n # depth gainif m in[Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]: c1, c2 = ch[f], args[0]if c2 != no:# if not output c2 = make_divisible(c2 * gw,8) args =[c1, c2,*args[1:]]if m in[BottleneckCSP, C3, C3TR, C3Ghost]: args.insert(2, n)# number of repeats n =1elif m is nn.BatchNorm2d: args =[ch[f]]elif m is Concat: c2 =sum(ch[x]for x in f)elif m is Detect: args.append([ch[x]for x in f])ifisinstance(args[1],int):# number of anchors args[1]=[list(range(args[1]*2))]*len(f)elif m is Contract: c2 = ch[f]* args[0]**2elif m is Expand: c2 = ch[f]// args[0]**2else: c2 = ch[f] m_ = nn.Sequential(*(m(*args)for _ inrange(n)))if n >1else m(*args)# module t =str(m)[8:-2].replace('__main__.','')# module type np =sum(x.numel()for x in m_.parameters())# number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}{t:<40}{str(args):<30}')# print save.extend(x % i for x in([f]ifisinstance(f,int)else f)if x !=-1)# append to savelist layers.append(m_)if i ==0: ch =[] ch.append(c2)return nn.Sequential(*layers),sorted(save)

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