1，融合pose.pt(姿态检测)+(安全帽佩戴检测)效果图

实时检测优化后FPS可达20+

2,原理介绍

YOLOv5是目前应用广泛的目标检测算法之一，其主要结构分为两个部分：骨干网络和检测头。

输入（Input）: YOLOv5的输入是一张RGB图像，它可以具有不同的分辨率，但通常为416x416或512x512像素。这些图像被预处理和缩放为神经网络的输入大小。在训练过程中，可以使用数据增强技术对图像进行随机裁剪、缩放和翻转等操作，以增加数据的丰富性和多样性。

Backbone（主干网络）: 主干网络负责提取图像的特征表示，它是整个目标检测算法的核心组件。YOLOv5采用了CSPDarknet作为主干网络。CSPDarknet基于Darknet53并进行了改进。它使用了一种被称为CSP（Cross Stage Partial）的结构，将特征映射划分为两个部分，其中一个部分通过一系列卷积和残差连接进行特征提取，另一个部分则直接传递未经处理的特征，从而提高了特征的表达能力和信息传递效率。

Neck（特征融合模块）: Neck模块用于融合来自不同层级的特征图，以获取丰富的语义信息和多尺度感受野。YOLOv5中采用了一种名为PANet（Path Aggregation Network）的结构作为Neck模块。PANet由两个阶段组成：自顶向下路径和自底向上路径。自顶向下路径负责从高级语义层级向低级语义层级传递信息，而自底向上路径则负责从低级语义层级向高级语义层级传递细节信息。通过这样的设计，PANet能够充分利用多层次的特征表示，并有效地融合不同层级的信息。

输出（Output）: YOLOv5的输出是目标检测算法的最终结果，包括检测框（bounding box）、置信度和类别信息。输出的过程经历了一系列的卷积和激活操作。首先，每个网格单元预测一组锚框，每个锚框包含了物体的位置和大小信息。然后，根据锚框和预测的边界框，计算出目标的置信度，反映了该边界框中是否存在目标的概率。最后，使用softmax函数对每个锚框预测的类别分数进行归一化，得到物体属于每个类别的概率。通过这样的输出，可以得到图像中检测到的目标的位置、类别和置信度信息。

3,优化前

虽然实现了效果但是FPS达不到实时检测的要求

4，优化后

FPS优化后达20+，满足实时检测需求，画面显示流畅

5，核心检测器代码

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.Usage - sources:$ python detect.py --weights yolov5s.pt --source 0                               # webcamimg.jpg                         # imagevid.mp4                         # videoscreen                          # screenshotpath/                           # directorylist.txt                        # list of imageslist.streams                    # list of streams'path/*.jpg'                    # glob'https://youtu.be/LNwODJXcvt4'  # YouTube'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streamUsage - formats:$ python detect.py --weights yolov5s.pt                 # PyTorchyolov5s.torchscript        # TorchScriptyolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnnyolov5s_openvino_model     # OpenVINOyolov5s.engine             # TensorRTyolov5s.mlmodel            # CoreML (macOS-only)yolov5s_saved_model        # TensorFlow SavedModelyolov5s.pb                 # TensorFlow GraphDefyolov5s.tflite             # TensorFlow Liteyolov5s_edgetpu.tflite     # TensorFlow Edge TPUyolov5s_paddle_model       # PaddlePaddle
"""import argparse
import csv
import os
import platform
import sys
from pathlib import Pathimport torchFILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relativefrom ultralytics.utils.plotting import Annotator, colors, save_one_boxfrom models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
from utils.torch_utils import select_device, smart_inference_mode@smart_inference_mode()
def run(weights=ROOT / 'yolov5s.pt',  # model path or triton URLsource=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)data=ROOT / 'data/coco128.yaml',  # dataset.yaml pathimgsz=(640, 640),  # inference size (height, width)conf_thres=0.25,  # confidence thresholdiou_thres=0.45,  # NMS IOU thresholdmax_det=1000,  # maximum detections per imagedevice='',  # cuda device, i.e. 0 or 0,1,2,3 or cpuview_img=False,  # show resultssave_txt=False,  # save results to *.txtsave_csv=False,  # save results in CSV formatsave_conf=False,  # save confidences in --save-txt labelssave_crop=False,  # save cropped prediction boxesnosave=False,  # do not save images/videosclasses=None,  # filter by class: --class 0, or --class 0 2 3agnostic_nms=False,  # class-agnostic NMSaugment=False,  # augmented inferencevisualize=False,  # visualize featuresupdate=False,  # update all modelsproject=ROOT / 'runs/detect',  # save results to project/namename='exp',  # save results to project/nameexist_ok=False,  # existing project/name ok, do not incrementline_thickness=3,  # bounding box thickness (pixels)hide_labels=False,  # hide labelshide_conf=False,  # hide confidenceshalf=False,  # use FP16 half-precision inferencednn=False,  # use OpenCV DNN for ONNX inferencevid_stride=1,  # video frame-rate stride
):source = str(source)save_img = not nosave and not source.endswith('.txt')  # save inference imagesis_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)screenshot = source.lower().startswith('screen')if is_url and is_file:source = check_file(source)  # download# Directoriessave_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir# Load modeldevice = select_device(device)model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)stride, names, pt = model.stride, model.names, model.ptimgsz = check_img_size(imgsz, s=stride)  # check image size# Dataloaderbs = 1  # batch_sizeif webcam:view_img = check_imshow(warn=True)dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)bs = len(dataset)elif screenshot:dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)else:dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)vid_path, vid_writer = [None] * bs, [None] * bs# Run inferencemodel.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmupseen, windows, dt = 0, [], (Profile(), Profile(), Profile())for path, im, im0s, vid_cap, s in dataset:with dt[0]:im = torch.from_numpy(im).to(model.device)im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32im /= 255  # 0 - 255 to 0.0 - 1.0if len(im.shape) == 3:im = im[None]  # expand for batch dim# Inferencewith dt[1]:visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else Falsepred = model(im, augment=augment, visualize=visualize)# NMSwith dt[2]:pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)# Second-stage classifier (optional)# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)# Define the path for the CSV filecsv_path = save_dir / 'predictions.csv'# Create or append to the CSV filedef write_to_csv(image_name, prediction, confidence):data = {'Image Name': image_name, 'Prediction': prediction, 'Confidence': confidence}with open(csv_path, mode='a', newline='') as f:writer = csv.DictWriter(f, fieldnames=data.keys())if not csv_path.is_file():writer.writeheader()writer.writerow(data)# Process predictionsfor i, det in enumerate(pred):  # per imageseen += 1if webcam:  # batch_size >= 1p, im0, frame = path[i], im0s[i].copy(), dataset.counts += f'{i}: 'else:p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)p = Path(p)  # to Pathsave_path = str(save_dir / p.name)  # im.jpgtxt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txts += '%gx%g ' % im.shape[2:]  # print stringgn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhimc = im0.copy() if save_crop else im0  # for save_cropannotator = Annotator(im0, line_width=line_thickness, example=str(names))if len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()# Print resultsfor c in det[:, 5].unique():n = (det[:, 5] == c).sum()  # detections per classs += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string# Write resultsfor *xyxy, conf, cls in reversed(det):c = int(cls)  # integer classlabel = names[c] if hide_conf else f'{names[c]}'confidence = float(conf)confidence_str = f'{confidence:.2f}'if save_csv:write_to_csv(p.name, label, confidence_str)if save_txt:  # Write to filexywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywhline = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label formatwith open(f'{txt_path}.txt', 'a') as f:f.write(('%g ' * len(line)).rstrip() % line + '\n')if save_img or save_crop or view_img:  # Add bbox to imagec = int(cls)  # integer classlabel = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')annotator.box_label(xyxy, label, color=colors(c, True))if save_crop:save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)# Stream resultsim0 = annotator.result()if view_img:if platform.system() == 'Linux' and p not in windows:windows.append(p)cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])cv2.imshow(str(p), im0)cv2.waitKey(1)  # 1 millisecond# Save results (image with detections)if save_img:if dataset.mode == 'image':cv2.imwrite(save_path, im0)else:  # 'video' or 'stream'if vid_path[i] != save_path:  # new videovid_path[i] = save_pathif isinstance(vid_writer[i], cv2.VideoWriter):vid_writer[i].release()  # release previous video writerif vid_cap:  # videofps = vid_cap.get(cv2.CAP_PROP_FPS)w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))else:  # streamfps, w, h = 30, im0.shape[1], im0.shape[0]save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videosvid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))vid_writer[i].write(im0)# Print time (inference-only)LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")# Print resultst = tuple(x.t / seen * 1E3 for x in dt)  # speeds per imageLOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)if save_txt or save_img:s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")if update:strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)def parse_opt():parser = argparse.ArgumentParser()parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path or triton URL')parser.add_argument('--source', type=str, default=ROOT / 'data/images/bus.jpg', help='file/dir/URL/glob/screen/0(webcam)')parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')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='show results')parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')parser.add_argument('--save-csv', action='store_true', help='save results in CSV format')parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')parser.add_argument('--nosave', action='store_true', help='do not save images/videos')parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 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('--visualize', action='store_true', help='visualize features')parser.add_argument('--update', action='store_true', help='update all models')parser.add_argument('--project', default=ROOT / 'runs/detect', 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('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')opt = parser.parse_args()opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expandprint_args(vars(opt))return optdef main(opt):check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))run(**vars(opt))if __name__ == '__main__':opt = parse_opt()main(opt)