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如何使用YOLOv11进行目标检测
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介绍
继YOLOv 8、YOLOv 9和YOLOv10之后，最近刚发布了最新的YOLOv11！这一新的迭代不仅建立在其版本的优势之上，而且还引入了几个突破性的增强功能，为目标检测和计算机视觉设定了新的基准。
与以前的版本一样，YOLOv 11擅长检测、分类和定位图像和视频中的对象。然而，它更进一步，通过整合显著的增强功能，提高了跨多个用例的性能和适应性。让我们来看看使YOLOv 11在该系列中脱颖而出的关键增强功能。
YOLOv11关键创新
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增强的特征提取：YOLOv11使用改进的主干和颈部架构，显著提高了特征提取能力。这导致更准确的物体检测和更轻松地处理复杂视觉任务的能力。
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针对效率和速度进行了优化：凭借精致的架构设计和优化的训练管道，YOLOv11在保持高精度的同时提供更快的处理速度。这种平衡确保了YOLOv11是实时和大规模应用的理想选择。
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更高的精度，更少的参数：YOLOv11m是YOLOv11的一个中等大小的变体，在COCO数据集上实现了更高的平均精度（mAP），同时使用的参数比YOLOv8m少22%。这种改进使其在不影响性能的情况下提高了计算效率。
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跨环境的适应性：无论是部署在边缘设备、云平台还是由NVIDIA GPU驱动的系统上，YOLOv11都能为各种部署场景提供最大的灵活性。
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广泛的支持任务：YOLOv 11将其功能扩展到传统的对象检测之外，以支持实例分割，图像分类，姿态估计和面向对象检测（OBB）。这种多功能性使其成为应对各种计算机视觉挑战的强大工具。
这些增强功能的集成使YOLOv 11成为尖端计算机视觉应用的强大引擎。请继续关注，我们将探索YOLOv 11如何突破这个动态领域的可能界限！
如何将YOLOv 11用于图像检测
步骤1：安装必要的库
pip install opencv-python ultralytics步骤2：导入库
import cv2
from ultralytics import YOLO步骤3：选择模型型号
model = YOLO("yolo11x.pt")在这个网站上:，您可以比较不同的模型，并权衡各自的优点和缺点。在这种情况下，我们选择yolov11x.pt。
步骤4：编写一个函数来预测和检测图像中的对象
def predict(chosen_model, img, classes=[], conf=0.5):
if classes:
results = chosen_model.predict(img, classes=classes, conf=conf)
else:
results = chosen_model.predict(img, conf=conf)
return results
def predict_and_detect(chosen_model, img, classes=[], conf=0.5, rectangle_thickness=2, text_thickness=1):
results = predict(chosen_model, img, classes, conf=conf)
for result in results:
for box in result.boxes:
cv2.rectangle(img, (int(box.xyxy[0][0]), int(box.xyxy[0][1])),
(int(box.xyxy[0][2]), int(box.xyxy[0][3])), (255, 0, 0), rectangle_thickness)
cv2.putText(img, f"{result.names[int(box.cls[0])]}",
(int(box.xyxy[0][0]), int(box.xyxy[0][1]) - 10),
cv2.FONT_HERSHEY_PLAIN, 1, (255, 0, 0), text_thickness)
return img, results步骤5：使用YOLOv11检测图像中的对象
# read the image
image = cv2.imread("YourImagePath")
result_img, _ = predict_and_detect(model, image, conf=0.5)步骤6：保存并绘制结果图像
cv2.imshow("Image", result_img)
cv2.imwrite("YourSavePath", result_img)
cv2.waitKey(0)完整代码：
from ultralytics import YOLO
import cv2
def predict(chosen_model, img, classes=[], conf=0.5):
if classes:
results = chosen_model.predict(img, classes=classes, conf=conf)
else:
results = chosen_model.predict(img, conf=conf)
return results
def predict_and_detect(chosen_model, img, classes=[], conf=0.5, rectangle_thickness=2, text_thickness=1):
results = predict(chosen_model, img, classes, conf=conf)
for result in results:
for box in result.boxes:
cv2.rectangle(img, (int(box.xyxy[0][0]), int(box.xyxy[0][1])),
(int(box.xyxy[0][2]), int(box.xyxy[0][3])), (255, 0, 0), rectangle_thickness)
cv2.putText(img, f"{result.names[int(box.cls[0])]}",
(int(box.xyxy[0][0]), int(box.xyxy[0][1]) - 10),
cv2.FONT_HERSHEY_PLAIN, 1, (255, 0, 0), text_thickness)
return img, results
model = YOLO("yolo11x.pt")
# read the image
image = cv2.imread("YourImagePath.png")
result_img, _ = predict_and_detect(model, image, classes=[], conf=0.5)
cv2.imshow("Image", result_img)
cv2.imwrite("YourSavePath.png", result_img)
cv2.waitKey(0)如何将YOLOv11用于视频检测
步骤1：安装必要的库
pip install opencv-python ultralytics步骤2和3：导入库与模型
import cv2
from ultralytics import YOLO
model = YOLO("yolo11x.pt")步骤4：创建Videowriter以保存视频的结果
# defining function for creating a writer (for mp4 videos)
def create_video_writer(video_cap, output_filename):
# grab the width, height, and fps of the frames in the video stream.
frame_width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(video_cap.get(cv2.CAP_PROP_FPS))
# initialize the FourCC and a video writer object
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
writer = cv2.VideoWriter(output_filename, fourcc, fps,
(frame_width, frame_height))
return writer步骤5：使用YOLOv 11检测视频中的对象
output_filename = "YourFilename.mp4"
video_path = r"YourVideoPath.mp4"
cap = cv2.VideoCapture(video_path)
writer = create_video_writer(cap, output_filename)
while True:
success, img = cap.read()
if not success:
break
result_img, _ = predict_and_detect(model, img, classes=[], conf=0.5)
writer.write(result_img)
cv2.imshow("Image", result_img)
cv2.waitKey(1)
writer.release()完整代码
import cv2
from ultralytics import YOLO
def predict(chosen_model, img, classes=[], conf=0.5):
if classes:
results = chosen_model.predict(img, classes=classes, conf=conf)
else:
results = chosen_model.predict(img, conf=conf)
return results
def predict_and_detect(chosen_model, img, classes=[], conf=0.5, rectangle_thickness=2, text_thickness=1):
results = predict(chosen_model, img, classes, conf=conf)
for result in results:
for box in result.boxes:
cv2.rectangle(img, (int(box.xyxy[0][0]), int(box.xyxy[0][1])),
(int(box.xyxy[0][2]), int(box.xyxy[0][3])), (255, 0, 0), rectangle_thickness)
cv2.putText(img, f"{result.names[int(box.cls[0])]}",
(int(box.xyxy[0][0]), int(box.xyxy[0][1]) - 10),
cv2.FONT_HERSHEY_PLAIN, 1, (255, 0, 0), text_thickness)
return img, results
# defining function for creating a writer (for mp4 videos)
def create_video_writer(video_cap, output_filename):
# grab the width, height, and fps of the frames in the video stream.
frame_width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(video_cap.get(cv2.CAP_PROP_FPS))
# initialize the FourCC and a video writer object
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
writer = cv2.VideoWriter(output_filename, fourcc, fps,
(frame_width, frame_height))
return writer
model = YOLO("yolo11x.pt")
output_filename = "YourFilename.mp4"
video_path = r"YourVideoPath.mp4"
cap = cv2.VideoCapture(video_path)
writer = create_video_writer(cap, output_filename)
while True:
success, img = cap.read()
if not success:
break
result_img, _ = predict_and_detect(model, img, classes=[], conf=0.5)
writer.write(result_img)
cv2.imshow("Image", result_img)
cv2.waitKey(1)
writer.release()结论
在本教程中，我们学习了如何使用YOLOv 11检测图像和视频中的对象。如果你觉得这段代码很有帮助，感谢点赞关注！