0 前言

🔥 优质竞赛项目系列，今天要分享的是

🚩 基于深度学习的中国交通标志识别算法研究与实现

该项目较为新颖，适合作为竞赛课题方向，学长非常推荐！

🥇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

🧿 更多资料, 项目分享：

https://gitee.com/dancheng-senior/postgraduate

1 yolov5实现中国交通标志检测

整个互联网基本没有国内交通标志识别的开源项目(都是国外的)，今天学长分享一个中国版本的实时交通标志识别项目，非常适合作为毕业设计~

2.算法原理

2.1 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。

2.2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。

2.3 关键代码

​

    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)

3 数据集处理

中国交通标志检测数据集CCTSDB，由长沙理工大学提供，包括上万张有标注的图片

推荐只使用前4000张照片，因为后面有很多张图片没有标注，需要一张一张的删除，太过于麻烦，所以尽量用前4000张图

3.1 VOC格式介绍

VOC格式主要包含三个文件夹Annotations，ImageSets，JPEGImages，主要适用于faster-
rcnn等模型的训练，ImageSets下面有一个Main的文件夹，如下图，一定按照这个名字和格式建好文件夹：

• Annotations：这里是存放你对所有数据图片做的标注，每张照片的标注信息必须是xml格式。

• JPEGImages：用来保存你的数据图片，一定要对图片进行编号，一般按照voc数据集格式，采用六位数字编码，如000001.jpg、000002.jpg等。

• ImageSets：该文件下有一个main文件，main文件下有四个txt文件，分别是train.txt、test.txt、trainval.txt、val.txt，里面都是存放的图片号码。

3.2 将中国交通标志检测数据集CCTSDB数据转换成VOC数据格式

将标注的数据提取出来并且排序，并将里面每一行分割成一个文件

3.3 手动标注数据集

如果为了更深入的学习也可自己标注，但过程相对比较繁琐，麻烦。

以下简单介绍数据标注的相关方法，数据标注这里推荐的软件是labelimg，通过pip指令即可安装，相关教程可网上搜索

​
pip install labelimg

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

5.1 视频效果

6 最后

🧿 更多资料, 项目分享：

https://gitee.com/dancheng-senior/postgraduate