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温岭网站建设,成人用品网店进货渠道,网站建设和推广需要多少费用,seo需求项目简介基于Yolo11的无人机小目标检测系统的设计与性能优化改进的目标检测项目名称基于Yolo11的无人机小目标检测系统的设计与性能优化改进项目简介该项目旨在开发一个基于YOLO11的无人机目标检测系统，能够实时识别并定位无人机拍摄过程中捕捉的小目标。…

项目简介

基于Yolo11的无人机小目标检测系统的设计与性能优化改进的目标检测

项目名称

基于Yolo11的无人机小目标检测系统的设计与性能优化改进

项目简介

该项目旨在开发一个基于YOLO11的无人机目标检测系统，能够实时识别并定位无人机拍摄过程中捕捉的小目标。考虑到无人机拍摄的目标通常较小，系统将采用特定的调优策略，以提高小目标的检测精度和召回率。

数据

数据集下载

https://github.com/VisDrone/VisDrone-Dataset

数据预处理

1.获取数据集的labels目标框标签

训练集数据

from PIL import Image
from tqdm import tqdm
from pathlib import Path
import os
def labelsplit(path):# 假设 dir 是一个 Path 对象，指向你想要处理的目录# 获取当前路径# d:\huaqing\code\detect_plane_project_4current = os.path.dirname(__file__)txt_dir = os.path.join(current, 'datasets', path, 'annotations')img_dir = os.path.join(current, 'datasets', path, 'images')labels_dir = os.path.join(current, 'datasets', path, 'labels')
# 转化为相对路径# txt的文件路径txt_path = os.path.relpath(txt_dir)img_path = os.path.relpath(img_dir)labels_path = os.path.relpath(labels_dir)

txt_path = Path(txt_path)# 遍历txt文件夹下的所有txt文件# pbar = os.listdir(txt_path)pbar = tqdm((txt_path).glob('*.txt'), desc=f'Converting {txt_path}')for f in pbar:# 【对图片的操作】# 构建对应的图像文件路径并获取尺寸txt_name = f.name.split('.')[0]img_path_jpg = Path(os.path.join(img_path, f.name)).with_suffix('.jpg')    img_size = Image.open(img_path_jpg).size
# 路径是否存在检测if not (os.path.exists(img_path) and os.path.exists(img_path) and os.path.exists(labels_path) and os.path.exists(img_path_jpg)):print("Warning: Image file not found")

lines = []with open(f, 'r') as file:# read annotation.txtfor row in [x.split(',') for x in file.read().strip().splitlines()]:# 0表示无效狂，所以舍去if row[4] == '0':  # VisDrone 'ignored regions' class 0continue# 目标1索引cls = int(row[5]) - 1# # 中心点坐标center_x = (float(row[0]) + (float(row[2]) / 2)) / img_size[0]center_y = (float(row[1]) + (float(row[3]) / 2)) / img_size[1]labels_txt = str(cls) + ' ' + str(center_x) + ' ' + str(center_y) + ' ' + str(float(row[2]) / img_size[0]) + ' ' + str(float(row[3]) / img_size[1])lines.append(labels_txt)with open(os.path.join(labels_path, txt_name + '.txt'), 'w') as file:for i in range(len(lines)):file.write(lines[i])file.write('\n')file.close()

if __name__ == '__main__':labelsplit('VisDrone2019-DET-train')

测试集数据（修改传入的文件路径，其余代码一致）

if __name__ == '__main__':labelsplit('VisDrone2019-DET-val')

模型训练

编写数据集的配置文件

# Ultralytics YOLO 🚀, AGPL-3.0 license
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
# Documentation: https://docs.ultralytics.com/datasets/detect/visdrone/
# Example usage: yolo train data=VisDrone.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── VisDrone  ← downloads here (2.3 GB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ./datasets # dataset root dir
train: VisDrone2019-DET-train # train images (relative to 'path')  6471 images
val: VisDrone2019-DET-val # val images (relative to 'path')  548 images
test: VisDrone2019-DET-test-dev # test images (optional)  1610 images

# Classes
names:0: pedestrian1: people2: bicycle3: car4: van5: truck6: tricycle7: awning-tricycle8: bus9: motor10.other

修改模型的检测分类数

车牌目标检测只检测车牌，因此模型输出分类数为1

nc: 10  # number of classes

模型参数的修改

# 1.【增加网络层】
# YOLO11n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 2, C3k2, [256, False, 0.25]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 2, C3k2, [512, False, 0.25]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 2, C3k2, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 2, C3k2, [1024, True]]- [-1, 2, CBAM, [1024, 7]] # 新增CBAM，后面的层级自然都+1- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 2, C2PSA, [1024]] # 10

#2.【修改网络层级关系，因为是在第九层加入的CBAM，所以九层以后的层级需要加一层】
# YOLO11n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]- [[-1, 14], 1, Concat, [1]] # cat head P4 13->14- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]- [[-1, 11], 1, Concat, [1]] # cat head P5 10->11- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)
16->17 19->20 22->23
# 3.【yolo11本身写有CBAM，所以只需要逐层将其导出就行】

训练模型权重

yolo detect train data=cfg\datasets\VisDrone.yamlmodel=ultralytics\ultralytics\cfg\models\11\yolo11m.yaml  epochs=30 imgsz=640

训练过程可视化

不同训练轮次下各类训练指标的折线图

第一幅图：在不同的分类中的训练预测出的数量,其中车辆的检测目标最多，摩托车检测的数据最少
第二幅图：预测框的形状情况，由图可以看出小中目标居多
第三幅图：预测框的中心坐标的分布情况
第四幅图：预测框的长宽分布情况

模型验证

yolo detect val model=.\runs\detect\train23\weights\best.pt data=.\data\VisDrone.yaml

模型应用

模型加载并使用

from ultralytics import YOLO

model = YOLO(r'..\runs\detect\trian22\weights\best.pt')
model.predict(r"..\datasets\VisDrone2019-DET-test-challenge\images\0000000_02309_d_0000006.jpg", save=True)