基于yolov5的智慧交通监测系统

基于yolov5的智慧交通监测系统

 本项目实现了智慧交通监测、红绿灯监测、行人监测、车辆识别、斑马线闯红灯监测等多种监测功能。

目录

背景

 演示效果：

 检测代码样例：

最后的检测效果如图所示

项目具体的工作流程为：

 总结：

下载链接 ：

注意：源码文件过大。共分为4个卷！！！ 须同时下载放到一个文件夹内解压

背景

针对城市交通拥堵问题，提出了一种基于yolov5的智慧交通监测系统。该系统利用了yolov5的智能感知、云计算以及通信等功能，实现车辆的定位跟踪和远程监控。系统首先通过yolov5摄像头采集车辆实时位置信息，然后将采集到的信息以数据流和图像流形式展示给客户端。实验结果表明，该系统可实时监测车辆的行驶轨迹和位置信息，具有一定的实用价值。

 演示效果：

python闯红灯检测斑马线检测红绿灯检测车速检测车流量统计车牌识别智慧交通系统

 检测代码样例：

import argparse
import time
from pathlib import Pathimport cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import perimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
al import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
h_utils import select_device, load_classifier, time_synchronized, TracedModeldef detect(save_img=False):source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, _tracesave_img = save and dswith('.txt')  # save inference images# Directoriessave_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok&#ist_ok))  # increment run(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir# Initializeset_logging()device = select_device(opt.device)half = pe != 'cpu'  # half precision only supported on CUDA# Load modelmodel = attempt_load(weights, map_location=device)  # load FP32 modelstride = int(model.stride.max())  # model strideimgsz = check_img_size(imgsz, s=stride)  # check img_sizeif trace:model = TracedModel(model, device, opt.img_size)if half:model.half()  # to FP16# Second-stage classifierclassify = Falseif classify:modelc = load_classifier(name='resnet101', n=2)  # initializemodelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()# Set Dataloadervid_path, vid_writer = None, Nonedataset = LoadImages(source, img_size=imgsz, stride=stride)# Get names and colorsnames = dule.names if hasattr(model, 'module') else model.namescolors = [[random.randint(0, 255) for _ in range(3)] for _ in names]# Run pe != 'cpu':s(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run onceold_img_w = old_img_h = imgszold_img_b = 1t0 = time.time()for path, img, im0s, vid_cap in dataset:img = torch.from_numpy(img).to(device)img = img.half() if half else img.float()  # uint8 to fp16/32img /= 255.0  # 0 - 255 to 0.0 - 1.0if img.ndimension() == 3:img = img.unsqueeze(0)# pe != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):old_img_b = img.shape[0]old_img_h = img.shape[2]old_img_w = img.shape[3]for i in range(3):model(img, augment=opt.augment)[0]# Inferencet1 = time_synchronized()pred = model(img, augment=opt.augment)[0]t2 = time_synchronized()# Apply NMSpred = non_max_suppression(pred, f_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)t3 = time_synchronized()# Apply Classifierif classify:pred = apply_classifier(pred, modelc, img, im0s)# Process detectionsfor i, det in enumerate(pred):  # detections per imagep, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)p = Path(p)  # to Pathsave_path = str(save_dir / p.name)  # img.jpgtxt_path = str(save_dir / 'labels' / p.stem) + ('' de == 'image' else f'_{frame}')  #  = sor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhif len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()# Print resultsfor c in det[:, -1].unique():n = (det[:, -1] == c).sum()  # detections per classif names[int(c)] == 'car'or names[int(c)] == 'bus' or names[int(c)] == 'truck':s += f"{n} {'vehicle'}{'s' * (n > 1)}, "  # add to string# Write resultsfor *xyxy, conf, cls in reversed(det):if save_txt:  # Write to filexywh = (sor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywhline = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label formatwith open(txt_path + '.txt', 'a') as f:f.write(('%g ' * len(line)).rstrip() % line + 'n')if save_img or view_img:  # Add bbox to imagelabel = f'{names[int(cls)]} {conf:.2f}'# print('label:',label)if "car" in label or "bus" in label or "truck" in label:# plot_one_box(xyxy, im0, label='vehicle', color=colors[int(cls)], line_thickness=1)plot_one_box(xyxy, im0, label='vehicle', color=colors[0], line_thickness=1)# Print time (inference + NMS)print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')# Stream resultsif view_img:cv2.imshow(str(p), im0)cv2.waitKey(0)  # 1 millisecond# Save results (image with detections)if save_img:de == 'image':cv2.imwrite(save_path, im0)print(f" The image with the result is saved in: {save_path}")print(f'Done. ({time.time() - t0:.3f}s)')if __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('--weights', nargs='+', type=str, default='car.pt', help='model.pt path(s)')parser.add_argument('--source', type=str, default='images', help='source')  # file/folder, 0 for webcamparser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')parser.add_argument('--iou-thres', type=float, default=0.45, 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')parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')parser.add_argument('--nosave', action='store_true', help='do not save images/videos')parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')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')parser.add_argument('--project', default='res_output', help='save results to project/name')parser.add_argument('--name', default='exp', help='save results to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')parser.add_argument('--no-trace', action='store_true', help='don`t trace model')opt = parser.parse_args()# print(opt)_grad():detect()

最后的检测效果如图所示

项目具体的工作流程为：

（a）建立了“智慧监控中心”的数据库，并根据用户需要对数据库进行了数据存储设计和数据分析与处理设计。

（b）客户端软件中的车载信息显示器实时显示车辆位置、速度以及车速等交通信息。

（c）利用yolov5对车辆运动轨迹进行实时追踪和记录，从而实现“智慧监控中心”中车辆实时位置数据与历史记录数据的同步传输。

（d）利用智能摄像头采集交通信号等数据并将其传输到服务器中进行处理和分析，从而实现远程控制和历史记录功能。

 总结：

随着我国城市化进程的加快，城市规模迅速扩大和人口数量的不断增加，交通拥堵问题日益严重，对居民日常生活及城市形象产生了巨大影响。本项目旨在推动智慧城市建设，利用信息化手段，不断为智慧城市创建提供新的想法和思路。

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