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1、毕业设计：2025年计算机专业毕业设计选题汇总（建议收藏）✅

2、大数据毕业设计：2025年选题大全 深度学习 python语言 JAVA语言 hadoop和spark（建议收藏）✅

1、项目介绍

技术栈：
python语言、YoloV5深度学习算法、pyqt图形模块、训练集测试集、目标检测
YOLO交通信号标志识别系统 深度学习 OpenCV 目标检测识别 大数据 毕业设计✅

本项目是面向智慧交通与计算机毕业设计需求开发的 YOLO 交通信号标志识别系统，以 Python 为开发语言，核心集成 YOLOv5 深度学习目标检测算法、PyQt 图形交互模块、OpenCV 图像处理技术，搭配专属训练集与测试集优化模型性能，构建具备交通信号标志精准识别、可视化交互、实时检测的一体化系统，可广泛应用于智能驾驶辅助、交通监控管控、道路安全预警等场景，解决传统交通信号识别依赖人工、效率低、易误判的问题，兼具技术先进性与实际应用价值，完全符合毕业设计的技术深度与功能完整性要求。​
技术架构上，项目形成 “数据支撑 - 模型检测 - 界面交互 - 图像处理” 的完整体系：YOLOv5 算法作为核心检测模型，凭借其高效的实时目标检测能力，能快速定位图像或视频中的交通信号标志（如限速、禁止、指示类标志），通过专属训练集（用于模型参数迭代优化）与测试集（验证识别准确率）的反复打磨，确保对不同光照、天气、遮挡场景下的标志识别精度；OpenCV 负责图像预处理（如降噪、增强、尺寸适配）与视频流解析，为 YOLOv5 模型提供高质量输入数据，提升检测稳定性；PyQt 搭建直观交互界面，实现模型调用、图像 / 视频导入、检测结果展示等功能，降低用户操作门槛；整体技术栈围绕 “目标检测” 核心，保障从数据处理到结果输出的全流程高效运转。​
核心功能聚焦 “多源检测 - 精准识别 - 可视化展示” 全场景：支持图像导入检测，用户通过 PyQt 界面点击 “选择文件”，导入交通场景图片后触发 YOLOv5 模型推理，系统自动标注信号标志位置（ bounding box ）、类别（如 “限速 60”“禁止通行”）及置信度，结果实时显示在界面；支持视频 / 摄像头实时检测，OpenCV 读取视频流或摄像头画面，逐帧传递给 YOLOv5 模型，实现动态场景下的信号标志实时识别，适用于道路监控、车载辅助等动态场景；同时，系统可记录检测数据，通过测试集持续验证模型性能，便于毕业设计中的效果分析与优化展示。​
系统界面设计清晰，功能分区明确（文件导入区、检测控制区、结果展示区），即使非技术用户也能快速上手。YOLOv5 模型推理速度快，检测准确率高，搭配 OpenCV 的图像处理优化，能适应复杂交通环境。项目融合深度学习目标检测、GUI 开发、图像处理技术，既展现了扎实的技术整合能力，又为智慧交通场景提供实用解决方案，是兼顾学术研究与实际应用的优质计算机毕业设计。

2、项目界面

（1）上传图片检测—限速70

（2）上传图片检测—限速40

（3）上传图片检测—行人

（4）上传图片检测—泊车

（5）摄像头实时检测—行人

（6）注册登录

（7）界面设计

3、项目说明

训练数据集包含6522张图片，验证集包含632张图片，测试集296张图片，共计7450张图片

本系统基于YOLOv5，采用登录注册进行用户管理，对于图片、视频和摄像头捕获的实时画面，可检测交通信号标志图像，系统支持结果记录、展示和保存，每次检测的结果记录在表格中。对此这里给出博主设计的界面，功能也可以满足图片、视频和摄像头的识别检测。

数据识别的分类：

‘40 Limit’: “限速40”, ‘50 Limit’: “限速50”, ‘60 Limit’: “限速60”, ‘70 Limit’: “限速70” ‘80 Limit’: “限速80”, ‘Give way’: “注意让行”, ‘No Entry’: “禁止驶入”, ‘Parking’: “泊车”, ‘Pedestrian’: “行人”, ‘Roundabout’: “环形交叉”, ‘stop’: “停车”}

4、核心代码

# -*- coding: utf-8 -*-
"""
@File ：train.py
@IDE ：PyCharm
功能：
"""
import argparse
import logging
import os
import random
import shutil
import time
from pathlib import Path
from warnings import warn

import math
import numpy as np
import torch.distributed as dist
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.utils.data
import yaml
from torch.cuda import amp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm

import test  # import test.py to get mAP after each epoch
from models.yolo import Model
from utils.datasets import create_dataloader
from utils.general import (
    torch_distributed_zero_first, labels_to_class_weights, plot_labels, check_anchors, labels_to_image_weights,
    compute_loss, plot_images, fitness, strip_optimizer, plot_results, get_latest_run, check_dataset, check_file,
    check_git_status, check_img_size, increment_dir, print_mutation, plot_evolution, set_logging, init_seeds)
from utils.google_utils import attempt_download
from utils.torch_utils import ModelEMA, select_device, intersect_dicts

logger = logging.getLogger(__name__)


def train(hyp, opt, device, tb_writer=None):
    logger.info(f'Hyperparameters {hyp}')
    log_dir = Path(tb_writer.log_dir) if tb_writer else Path(opt.logdir) / 'evolve'  # logging directory
    wdir = log_dir / 'weights'  # weights directory
    os.makedirs(wdir, exist_ok=True)
    last = wdir / 'last.pt'
    best = wdir / 'best.pt'
    results_file = str(log_dir / 'results.txt')
    epochs, batch_size, total_batch_size, weights, rank = \
        opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank

    # Save run settings
    with open(log_dir / 'hyp.yaml', 'w',encoding='utf-8') as f:
        yaml.dump(hyp, f, sort_keys=False)
    with open(log_dir / 'opt.yaml', 'w',encoding='utf-8') as f:
        yaml.dump(vars(opt), f, sort_keys=False)

    # Configure
    cuda = device.type != 'cpu'
    init_seeds(2 + rank)
    with open(opt.data,encoding='utf-8') as f:
        data_dict = yaml.load(f, Loader=yaml.FullLoader)  # data dict
    with torch_distributed_zero_first(rank):
        check_dataset(data_dict)  # check
    train_path = data_dict['train']
    test_path = data_dict['val']
    nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names'])  # number classes, names
    assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check

    # Model
    pretrained = weights.endswith('.pt')
    if pretrained:
        with torch_distributed_zero_first(rank):
            attempt_download(weights)  # download if not found locally
        ckpt = torch.load(weights, map_location=device)  # load checkpoint
        if hyp.get('anchors'):
            ckpt['model'].yaml['anchors'] = round(hyp['anchors'])  # force autoanchor
        model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device)  # create
        exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else []  # exclude keys
        state_dict = ckpt['model'].float().state_dict()  # to FP32
        state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude)  # intersect
        model.load_state_dict(state_dict, strict=False)  # load
        logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights))  # report
    else:
        model = Model(opt.cfg, ch=3, nc=nc).to(device)  # create

    # Freeze
    freeze = ['', ]  # parameter names to freeze (full or partial)
    if any(freeze):
        for k, v in model.named_parameters():
            if any(x in k for x in freeze):
                # print('freezing %s' % k)
                v.requires_grad = False

    # Optimizer
    nbs = 64  # nominal batch size
    accumulate = max(round(nbs / total_batch_size), 1)  # accumulate loss before optimizing
    hyp['weight_decay'] *= total_batch_size * accumulate / nbs  # scale weight_decay

    pg0, pg1, pg2 = [], [], []  # optimizer parameter groups
    for k, v in model.named_parameters():
        v.requires_grad = True
        if '.bias' in k:
            pg2.append(v)  # biases
        elif '.weight' in k and '.bn' not in k:
            pg1.append(v)  # apply weight decay
        else:
            pg0.append(v)  # all else

    if opt.adam:
        optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
    else:
        optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)

    optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']})  # add pg1 with weight_decay
    optimizer.add_param_group({'params': pg2})  # add pg2 (biases)
    logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
    del pg0, pg1, pg2

    # Scheduler https://arxiv.org/pdf/1812.01187.pdf
    # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
    lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp['lrf']) + hyp['lrf']  # cosine
    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
    # plot_lr_scheduler(optimizer, scheduler, epochs)

    # Resume
    start_epoch, best_fitness = 0, 0.0
    if pretrained:
        # Optimizer
        if ckpt['optimizer'] is not None:
            optimizer.load_state_dict(ckpt['optimizer'])
            best_fitness = ckpt['best_fitness']

        # Results
        if ckpt.get('training_results') is not None:
            with open(results_file, 'w',encoding='utf-8') as file:
                file.write(ckpt['training_results'])  # write results.txt

        # Epochs
        start_epoch = ckpt['epoch'] + 1
        if opt.resume:
            assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
            shutil.copytree(wdir, wdir.parent / f'weights_backup_epoch{start_epoch - 1}')  # save previous weights
        if epochs < start_epoch:
            logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
                        (weights, ckpt['epoch'], epochs))
            epochs += ckpt['epoch']  # finetune additional epochs

        del ckpt, state_dict

    # Image sizes
    gs = int(max(model.stride))  # grid size (max stride)
    imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size]  # verify imgsz are gs-multiples

    # DP mode
    if cuda and rank == -1 and torch.cuda.device_count() > 1:
        model = torch.nn.DataParallel(model)

    # SyncBatchNorm
    if opt.sync_bn and cuda and rank != -1:
        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
        logger.info('Using SyncBatchNorm()')

    # Exponential moving average
    ema = ModelEMA(model) if rank in [-1, 0] else None

    # DDP mode
    if cuda and rank != -1:
        model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank)

    # Trainloader
    dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
                                            hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect,
                                            rank=rank, world_size=opt.world_size, workers=opt.workers)
    mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label class
    nb = len(dataloader)  # number of batches
    assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)

    # Process 0
    if rank in [-1, 0]:
        ema.updates = start_epoch * nb // accumulate  # set EMA updates
        testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt,
                                       hyp=hyp, augment=False, cache=opt.cache_images and not opt.notest, rect=True,
                                       rank=-1, world_size=opt.world_size, workers=opt.workers)[0]  # testloader

        if not opt.resume:
            labels = np.concatenate(dataset.labels, 0)
            c = torch.tensor(labels[:, 0])  # classes
            # cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency
            # model._initialize_biases(cf.to(device))
            plot_labels(labels, save_dir=log_dir)
            if tb_writer:
                # tb_writer.add_hparams(hyp, {})  # causes duplicate https://github.com/ultralytics/yolov5/pull/384
                tb_writer.add_histogram('classes', c, 0)

            # Anchors
            if not opt.noautoanchor:
                check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)

    # Model parameters
    hyp['cls'] *= nc / 80.  # scale coco-tuned hyp['cls'] to current dataset
    model.nc = nc  # attach number of classes to model
    model.hyp = hyp  # attach hyperparameters to model
    model.gr = 1.0  # iou loss ratio (obj_loss = 1.0 or iou)
    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
    model.names = names

    # Start training
    t0 = time.time()
    nw = max(round(hyp['warmup_epochs'] * nb), 1e3)  # number of warmup iterations, max(3 epochs, 1k iterations)
    # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
    maps = np.zeros(nc)  # mAP per class
    results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
    scheduler.last_epoch = start_epoch - 1  # do not move
    scaler = amp.GradScaler(enabled=cuda)
    logger.info('Image sizes %g train, %g test\n'
                'Using %g dataloader workers\nLogging results to %s\n'
                'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, log_dir, epochs))
    for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
        model.train()

        # Update image weights (optional)
        if opt.image_weights:
            # Generate indices
            if rank in [-1, 0]:
                cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2  # class weights
                iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
                dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx
            # Broadcast if DDP
            if rank != -1:
                indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
                dist.broadcast(indices, 0)
                if rank != 0:
                    dataset.indices = indices.cpu().numpy()

        # Update mosaic border
        # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
        # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders

        mloss = torch.zeros(4, device=device)  # mean losses
        if rank != -1:
            dataloader.sampler.set_epoch(epoch)
        pbar = enumerate(dataloader)
        logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size'))
        if rank in [-1, 0]:
            pbar = tqdm(pbar, total=nb)  # progress bar
        optimizer.zero_grad()
        for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
            ni = i + nb * epoch  # number integrated batches (since train start)
            imgs = imgs.to(device, non_blocking=True).float() / 255.0  # uint8 to float32, 0-255 to 0.0-1.0

            # Warmup
            if ni <= nw:
                xi = [0, nw]  # x interp
                # model.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)
                accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
                for j, x in enumerate(optimizer.param_groups):
                    # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
                    x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
                    if 'momentum' in x:
                        x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])

            # Multi-scale
            if opt.multi_scale:
                sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs  # size
                sf = sz / max(imgs.shape[2:])  # scale factor
                if sf != 1:
                    ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]]  # new shape (stretched to gs-multiple)
                    imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)

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