• 【毕业设计】深度学习 YOLO 实现车牌识别算法



    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。

    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,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:

    https://gitee.com/dancheng-senior/project-sharing-1/blob/master/%E6%AF%95%E8%AE%BE%E6%8C%87%E5%AF%BC/README.md



    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

    在这里插入图片描述

    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。

    在这里插入图片描述

    4 模型训练

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

    在这里插入图片描述

    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。

    在这里插入图片描述

    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:
    
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    5.1 图片识别效果

    在这里插入图片描述

    5.2视频识别效果

    在这里插入图片描述

    6 部分关键代码

    篇幅有限,仅展示部分代码

    class Detect(nn.Module):
        stride = None  # strides computed during build
        onnx_dynamic = False  # ONNX export parameter
    
        def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
            super().__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)
    
        def forward(self, x):
            z = []  # inference output
            for i in range(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()
    
                if not self.training:  # inference
                    if 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]  # wh
                    else:  # 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
    
    
    class Model(nn.Module):
        def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
            super().__init__()
            if isinstance(cfg, dict):
                self.yaml = cfg  # model dict
            else:  # is *.yaml
                import yaml  # for torch hub
                self.yaml_file = Path(cfg).name
                with open(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 channels
            if 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 value
            if 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 in range(self.yaml['nc'])]  # default names
            self.inplace = self.yaml.get('inplace', True)
    
            # Build strides, anchors
            m = self.model[-1]  # Detect()
            if isinstance(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('')
    
        def forward(self, x, augment=False, profile=False, visualize=False):
            if augment:
                return self._forward_augment(x)  # augmented inference, None
            return self._forward_once(x, profile, visualize)  # single-scale inference, train
    
        def _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 = []  # outputs
            for si, fi in zip(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 tails
            return torch.cat(y, 1), None  # augmented inference, train
    
        def _forward_once(self, x, profile=False, visualize=False):
            y, dt = [], []  # outputs
            for m in self.model:
                if m.f != -1:  # if not from previous layer
                    x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
                if profile:
                    self._profile_one_layer(m, x, dt)
                x = m(x)  # run
                y.append(x if m.i in self.save else None)  # save output
                if 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-scale
                if flips == 2:
                    p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
                elif flips == 3:
                    p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
            else:
                x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
                if flips == 2:
                    y = img_size[0] - y  # de-flip ud
                elif 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 in range(nl))  # grid points
            e = 1  # exclude layer count
            i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
            y[0] = y[0][:, :-i]  # large
            i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
            y[-1] = y[-1][:, i:]  # small
            return 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 * 2 if thop else 0  # FLOPs
            t = time_sync()
            for _ in range(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() module
            for mi, s in zip(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 is None else 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() module
            for 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 weights
    
        def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
            LOGGER.info('Fusing layers... ')
            for m in self.model.modules():
                if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
                    m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                    delattr(m, 'bn')  # remove batchnorm
                    m.forward = m.forward_fuse  # update forward
            self.info()
            return self
    
        def autoshape(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 attributes
            return m
    
        def info(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()
            if isinstance(m, Detect):
                m.stride = fn(m.stride)
                m.grid = list(map(fn, m.grid))
                if isinstance(m.anchor_grid, list):
                    m.anchor_grid = list(map(fn, m.anchor_grid))
            return self
    
    
    def parse_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) if isinstance(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 out
        for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
            m = eval(m) if isinstance(m, str) else m  # eval strings
            for j, a in enumerate(args):
                try:
                    args[j] = eval(a) if isinstance(a, str) else a  # eval strings
                except NameError:
                    pass
    
            n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
            if 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 = 1
            elif 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])
                if isinstance(args[1], int):  # number of anchors
                    args[1] = [list(range(args[1] * 2))] * len(f)
            elif m is Contract:
                c2 = ch[f] * args[0] ** 2
            elif m is Expand:
                c2 = ch[f] // args[0] ** 2
            else:
                c2 = ch[f]
    
            m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else 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] if isinstance(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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    🧿 选题指导, 项目分享:

    https://gitee.com/dancheng-senior/project-sharing-1/blob/master/%E6%AF%95%E8%AE%BE%E6%8C%87%E5%AF%BC/README.md

    7 最后

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  • 原文地址:https://blog.csdn.net/HUXINY/article/details/125803915