视频封面制作网站,wordpress导出主题,网络运营是什么专业,国外设计网站d文章目录 0 前言1 yolov5实现中国交通标志检测2.算法原理2.1 算法简介2.2网络架构2.3 关键代码 3 数据集处理3.1 VOC格式介绍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3.3 手动标注数据集 4 模型训练5 实现效果5.1 视频效果 6 最后 0 前言
#x1f525; 优质… 文章目录 0 前言1 yolov5实现中国交通标志检测2.算法原理2.1 算法简介2.2网络架构2.3 关键代码 3 数据集处理3.1 VOC格式介绍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3.3 手动标注数据集 4 模型训练5 实现效果5.1 视频效果 6 最后 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FPNPAN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、FPNPAN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 buildonnx_dynamic False # ONNX export parameterdef __init__(self, nc80, anchors(), ch(), inplaceTrue): # detection layersuper().__init__()self.nc nc # number of classesself.no nc 5 # number of outputs per anchorself.nl len(anchors) # number of detection layersself.na len(anchors[0]) // 2 # number of anchorsself.grid [torch.zeros(1)] * self.nl # init gridself.anchor_grid [torch.zeros(1)] * self.nl # init anchor gridself.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 convself.inplace inplace # use in-place ops (e.g. slice assignment)def forward(self, x):z [] # inference outputfor i in range(self.nl):x[i] self.m[i](x[i]) # convbs, _, 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: # 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] # xyy[..., 2:4] (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # whelse: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953xy (y[..., 0:2] * 2 - 0.5 self.grid[i]) * self.stride[i] # xywh (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # why 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, nx20, ny20, i0):d self.anchors[i].deviceif check_version(torch.__version__, 1.10.0): # torch1.10.0 meshgrid workaround for torch0.7 compatibilityyv, xv torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)], indexingij)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_gridclass Model(nn.Module):def __init__(self, cfgyolov5s.yaml, ch3, ncNone, anchorsNone): # model, input channels, number of classessuper().__init__()if isinstance(cfg, dict):self.yaml cfg # model dictelse: # is *.yamlimport yaml # for torch hubself.yaml_file Path(cfg).namewith open(cfg, encodingascii, errorsignore) as f:self.yaml yaml.safe_load(f) # model dict# Define modelch self.yaml[ch] self.yaml.get(ch, ch) # input channelsif nc and nc ! self.yaml[nc]:LOGGER.info(fOverriding model.yaml nc{self.yaml[nc]} with nc{nc})self.yaml[nc] nc # override yaml valueif anchors:LOGGER.info(fOverriding model.yaml anchors with anchors{anchors})self.yaml[anchors] round(anchors) # override yaml valueself.model, self.save parse_model(deepcopy(self.yaml), ch[ch]) # model, savelistself.names [str(i) for i in range(self.yaml[nc])] # default namesself.inplace self.yaml.get(inplace, True)# Build strides, anchorsm self.model[-1] # Detect()if isinstance(m, Detect):s 256 # 2x min stridem.inplace self.inplacem.stride torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forwardm.anchors / m.stride.view(-1, 1, 1)check_anchor_order(m)self.stride m.strideself._initialize_biases() # only run once# Init weights, biasesinitialize_weights(self)self.info()LOGGER.info()def forward(self, x, augmentFalse, profileFalse, visualizeFalse):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, widths [1, 0.83, 0.67] # scalesf [None, 3, None] # flips (2-ud, 3-lr)y [] # outputsfor si, fi in zip(s, f):xi scale_img(x.flip(fi) if fi else x, si, gsint(self.stride.max()))yi self._forward_once(xi)[0] # forward# cv2.imwrite(fimg_{si}.jpg, 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # saveyi 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, profileFalse, visualizeFalse):y, dt [], [] # outputsfor m in self.model:if m.f ! -1: # if not from previous layerx y[m.f] if isinstance(m.f, int) else [x if j -1 else y[j] for j in m.f] # from earlier layersif profile:self._profile_one_layer(m, x, dt)x m(x) # runy.append(x if m.i in self.save else None) # save outputif visualize:feature_visualization(x, m.type, m.i, save_dirvisualize)return xdef _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 lrp torch.cat((x, y, wh, p[..., 4:]), -1)return pdef _clip_augmented(self, y):# Clip YOLOv5 augmented inference tailsnl self.model[-1].nl # number of detection layers (P3-P5)g sum(4 ** x for x in range(nl)) # grid pointse 1 # exclude layer counti (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indicesy[0] y[0][:, :-i] # largei (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indicesy[-1] y[-1][:, i:] # smallreturn ydef _profile_one_layer(self, m, x, dt):c isinstance(m, Detect) # is final layer, copy input as inplace fixo thop.profile(m, inputs(x.copy() if c else x,), verboseFalse)[0] / 1E9 * 2 if thop else 0 # FLOPst 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, cfNone): # 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(), minlengthnc) 1.m self.model[-1] # Detect() modulefor mi, s in zip(m.m, m.stride): # fromb 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()) # clsmi.bias torch.nn.Parameter(b.view(-1), requires_gradTrue)def _print_biases(self):m self.model[-1] # Detect() modulefor mi in m.m: # fromb 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 weightsdef fuse(self): # fuse model Conv2d() BatchNorm2d() layersLOGGER.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 convdelattr(m, bn) # remove batchnormm.forward m.forward_fuse # update forwardself.info()return selfdef autoshape(self): # add AutoShape moduleLOGGER.info(Adding AutoShape... )m AutoShape(self) # wrap modelcopy_attr(m, self, include(yaml, nc, hyp, names, stride), exclude()) # copy attributesreturn mdef info(self, verboseFalse, img_size640): # print model informationmodel_info(self, verbose, img_size)def _apply(self, fn):# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffersself 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 selfdef 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 anchorsno na * (nc 5) # number of outputs anchors * (classes 5)layers, save, c2 [], [], ch[-1] # layers, savelist, ch outfor i, (f, n, m, args) in enumerate(d[backbone] d[head]): # from, number, module, argsm eval(m) if isinstance(m, str) else m # eval stringsfor j, a in enumerate(args):try:args[j] eval(a) if isinstance(a, str) else a # eval stringsexcept NameError:passn n_ max(round(n * gd), 1) if n 1 else 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 outputc2 make_divisible(c2 * gw, 8)args [c1, c2, *args[1:]]if m in [BottleneckCSP, C3, C3TR, C3Ghost]:args.insert(2, n) # number of repeatsn 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])if isinstance(args[1], int): # number of anchorsargs[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 _ in range(n))) if n 1 else m(*args) # modulet str(m)[8:-2].replace(__main__., ) # module typenp sum(x.numel() for x in m_.parameters()) # number paramsm_.i, m_.f, m_.type, m_.np i, f, t, np # attach index, from index, type, number paramsLOGGER.info(f{i:3}{str(f):18}{n_:3}{np:10.0f} {t:40}{str(args):30}) # printsave.extend(x % i for x in ([f] if isinstance(f, int) else f) if x ! -1) # append to savelistlayers.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AnnotationsImageSetsJPEGImages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
