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1、项目介绍

技术栈：
Python语言、深度学习、PyTorch、YOLOv8、PySide界面设计、VisDrone数据集、无人机目标检测

基于 YOLOv8 与 VisDrone 数据集的无人机目标检测跟踪识别系统​
在安防监控、交通管理、灾害救援等领域，无人机凭借高空视角优势成为信息采集的重要工具，但如何高效处理无人机传回的图像、精准识别目标并实现跟踪，成为技术落地的关键。本项目聚焦无人机目标检测跟踪需求，以 Python 为开发核心，整合 PyTorch 深度学习框架、YOLOv8 目标检测算法、PySide 界面设计工具及 VisDrone 专业数据集，打造一套功能完善、精度优异的无人机目标检测跟踪识别系统，兼具技术创新性与实际应用价值，适用于计算机毕业设计课题。​
技术架构层面，项目以 PyTorch 为底层支撑，为 YOLOv8 算法的训练与推理提供高效计算环境。YOLOv8 作为当前主流的目标检测算法，相比前代模型在检测速度与精度上均有显著提升，能快速处理无人机采集的高分辨率图像，精准识别画面中的行人、车辆、建筑等目标，满足实时检测需求。考虑到无人机图像场景复杂、目标动态变化的特点，项目引入目标跟踪机制，与 YOLOv8 算法协同工作，实现对移动目标的持续追踪，避免目标丢失导致的检测中断。​
数据训练环节，项目选用 VisDrone 数据集作为模型训练基础。该数据集涵盖多种真实场景（如城市街道、乡村道路、灾害现场等），包含丰富的目标类别与标注信息，有效解决了无人机检测场景中数据稀缺、场景单一的问题，大幅提升模型的泛化能力。通过对数据集的预处理与迭代训练，模型在复杂背景、光照变化、目标遮挡等极端情况下，仍能保持较高的检测准确率，为系统稳定运行提供数据保障。​
界面与功能设计上，系统采用 PySide 框架搭建可视化交互界面，界面布局简洁清晰，包含图像 / 视频导入、实时检测、跟踪结果显示、数据导出等功能模块，用户无需复杂操作即可完成目标检测任务。同时，系统支持无人机实时回传画面的接入，能同步显示检测目标的位置、类别、置信度及跟踪轨迹，方便用户直观掌握目标动态。​
从应用价值来看，该系统可广泛服务于多领域：在安防监控中，可实时监测重点区域人员与车辆流动，及时预警异常情况；在交通管理中，能辅助统计车流量、识别违章车辆；在灾害救援中，可快速定位受困人员与救援目标，提升救援效率。作为毕业设计，项目不仅综合运用了深度学习、界面开发、数据处理等多学科知识，还通过实际场景落地验证了技术可行性，展现了较强的工程实践能力与创新思维。

2、项目界面

（1）无人机目标检测跟踪界面

（2）无人机目标检测跟踪界面2

（3）无人机目标检测跟踪界面3

（4）摄像头检测

3、项目说明

技术栈：
Python语言、深度学习、PyTorch、YOLOv8、PySide界面设计、VisDrone数据集、无人机目标检测

YOLOv8无人机目标检测跟踪识别系统 深度学习 PySide界面设计 大数据 毕业设计

YOLOv8无人机目标检测跟踪识别系统
本项目聚焦于无人机目标检测领域，运用前沿技术打造一套高效精准的检测跟踪识别系统。技术上，以 Python 为基础语言，借助 PyTorch 深度学习框架，采用先进的 YOLOv8 算法模型，该模型在目标检测方面具有出色的性能与准确性，能快速识别无人机图像中的各类目标。

系统采用 PySide 进行界面设计，为用户提供简洁直观、操作便捷的交互体验。数据方面，选用 VisDrone 数据集，其丰富的场景和多样的目标类别，有效保障了模型的泛化能力。通过大数据训练，模型不断优化，提升检测精度与速度。

此系统可广泛应用于安防监控、交通管理、灾害救援等场景，为相关领域提供实时、准确的目标信息。作为毕业设计项目，它不仅综合运用了深度学习、界面设计等多方面知识，还具备较高的实用价值与创新性，有望为无人机目标检测领域的发展贡献一份力量。

4、核心代码

from ultralytics import YOLO
from PySide6.QtWidgets import QApplication, QMainWindow, QFileDialog, QMenu
from PySide6.QtGui import QImage, QPixmap, QColor
from PySide6.QtCore import QTimer, QThread, Signal, QObject, QPoint, Qt

from UIFunctions import *
from ui.home import Ui_MainWindow

#from utils.capnums import Camera
from utils.rtsp_win import Window
from collections import deque
import numpy as np
import time
import json
import sys
import cv2
import os

IMG_FORMATS = ('bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm')  # image suffixes
VID_FORMATS = ('asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv', 'webm')  # video suffixes

def is_video_file(path):
    suffix = path.split('.')[-1].lower()

    if suffix in IMG_FORMATS:
        return False
    elif suffix in VID_FORMATS:
        return True
    else:
        print(f"Suffix '{suffix}' is invalid!")
        raise ValueError("Invalid file suffix")

class YoloPredictor(QObject):
    yolo2main_pre_img = Signal(np.ndarray)   # raw image signal
    yolo2main_res_img = Signal(np.ndarray)   # test result signal
    yolo2main_status_msg = Signal(str)       # Detecting/pausing/stopping/testing complete/error reporting signal
    yolo2main_fps = Signal(str)              # fps
    yolo2main_labels = Signal(dict)          # Detected target results (number of each category)
    yolo2main_progress = Signal(int)         # Completeness
    yolo2main_class_num = Signal(int)        # Number of categories detected
    yolo2main_target_num = Signal(int)       # Targets detected

    def __init__(self):
        super().__init__()

        # GUI args
        self.used_model_name = None      # The detection model name to use
        self.source = ''                 # input source
        self.stop_dtc = False            # Termination detection
        self.continue_dtc = True         # pause   
        self.labels_dict = {}            # return a dictionary of results
        self.progress_value = 0          # progress bar
        self.YoloConfig = dict()
        self.YoloConfig['model'] = None
        self.YoloConfig['iou'] = 0.70  # iou
        self.YoloConfig['conf'] = 0.25 # conf
        self.YoloConfig['rate'] = 30  # delay, ms
        self.YoloConfig['save_res'] = False  # Save test results
        self.YoloConfig['save_txt'] = False  # save label(txt) file

        # Usable if setup is done
        self.model = None
        self.imgsz = None
        self.device = None
        self.dataset = None
        self.vid_path, self.vid_writer = None, None
        self.data_path = None
        self.source_type = None
        self.batch = None

        self.fps_counter = 0
        self.fps_frames = deque(maxlen=10)  # 存储最近 10 帧的时间
        self.fps = 0

    def loadmodel(self):
        if not self.model or self.used_model_name != self.YoloConfig['model']:
            self.yolo2main_status_msg.emit('Loading Model...')
            print('model used = ', self.YoloConfig['model'])
            self.model = YOLO(self.YoloConfig['model'])
            self.used_model_name = self.YoloConfig['model']

    def run(self):
        print("running detection ......")
        # set model
        self.loadmodel()
        self.yolo2main_status_msg.emit('Detecting...')
        print('conf threshold = ', self.YoloConfig['conf'])
        print('iou threshold = ', self.YoloConfig['iou'])
        print('save = ', self.YoloConfig['save_res'])
        print('save_txt = ', self.YoloConfig['save_txt'])

        if not is_video_file(self.source):
            res = self.model.predict(self.source, save=self.YoloConfig['save_res'],
                                     save_txt=self.YoloConfig['save_txt'], imgsz=640, conf=self.YoloConfig['conf'],
                                     iou=self.YoloConfig['iou'], device='cpu')

            preprocess_speed = res[0].speed['preprocess']
            inference_speed = res[0].speed['inference']
            postprocess_speed = res[0].speed['postprocess']
            total_infer_speed = preprocess_speed + inference_speed + postprocess_speed
            fps = 1000 / total_infer_speed
            print("FPS:", fps)

            detected_boxes = res[0].boxes
            # print(res[0])

            print("detected boxes ......")
            # Create an empty list to store the class IDs
            class_ids = []
            # Iterate over each box and extract the class ID
            for box in detected_boxes:
                class_id = box.cls  # get the class id
                class_id_cpu = class_id.cpu()  # move the value to CPU
                class_id_int = int(class_id_cpu.item())  # convert to integer
                class_ids.append(class_id_int)  # append to the list

            # Print the class ID
            print("class ids = ", class_ids)
            total_classes = len(set(class_ids))
            total_ids = len(class_ids)
            # Send test results
            orig_img = res[0].orig_img
            annotated_img = res[0].plot()
            self.yolo2main_pre_img.emit(orig_img)  # Before testing
            self.yolo2main_res_img.emit(annotated_img)  # after detection
            # self.yolo2main_labels.emit(self.labels_dict)        # webcam need to change the def write_results
            self.yolo2main_class_num.emit(total_classes)
            self.yolo2main_target_num.emit(total_ids)
            self.yolo2main_fps.emit(str(int(fps)))
            self.yolo2main_status_msg.emit('Detection completed')

        else:
            cap = cv2.VideoCapture(self.source)

            # Loop through the video frames
            count = 0  # run location frame
            start_time = time.time()  # used to calculate the frame rate
            all_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)  # total frames
            while cap.isOpened():
                # Termination detection
                if self.stop_dtc:
                    self.yolo2main_status_msg.emit('Detection terminated!')
                    self.stop_dtc = False
                    break
                # Read a frame from the video
                success, frame = cap.read()
                count += 1  # frame count +1
                self.progress_value = int(count / all_count * 1000)  # progress bar(0~1000)
                self.yolo2main_progress.emit(self.progress_value)  # progress bar
                if count % 5 == 0 and count >= 5:  # Calculate the frame rate every 5 frames
                    self.yolo2main_fps.emit(str(int(5 / (time.time() - start_time))))
                    start_time = time.time()

       
    # Terminate button and associated state
    def stop(self):
        if self.yolo_thread.isRunning():
            self.yolo_thread.quit()         # end thread
        self.yolo_predict.stop_dtc = True
        self.run_button.setChecked(False)    # start key recovery
        self.save_res_button.setEnabled(True)   # Ability to use the save button
        self.save_txt_button.setEnabled(True)   # Ability to use the save button
        self.pre_video.clear()           # clear image display
        self.res_video.clear()           # clear image display
        self.progress_bar.setValue(0)
        self.Class_num.setText('--')
        self.Target_num.setText('--')
        self.fps_label.setText('--')

    # Change detection parameters
    def change_val(self, x, flag):
        if flag == 'iou_spinbox':
            self.iou_slider.setValue(int(x*100))    # The box value changes, changing the slider
        elif flag == 'iou_slider':
            self.iou_spinbox.setValue(x/100)        # The slider value changes, changing the box
            self.show_status('IOU Threshold: %s' % str(x/100))
            self.yolo_predict.YoloConfig['iou'] = x/100
        elif flag == 'conf_spinbox':
            self.conf_slider.setValue(int(x*100))
        elif flag == 'conf_slider':
            self.conf_spinbox.setValue(x/100)
            self.show_status('Conf Threshold: %s' % str(x/100))
            self.yolo_predict.YoloConfig['conf'] = x/100
        elif flag == 'speed_spinbox':
            self.speed_slider.setValue(x)
        elif flag == 'speed_slider':
            self.speed_spinbox.setValue(x)
            self.show_status('Delay: %s ms' % str(x))
            self.yolo_predict.YoloConfig['speed'] = x  # ms

    # change model
    def change_model(self,x):
        self.select_model = self.model_box.currentText()
        self.yolo_predict.YoloConfig['model'] = "./models/%s" % self.select_model
        self.show_status('Change Model：%s' % self.select_model)
        self.Model_name.setText(self.select_model)
        # load model signal emission
        self.main2yolo_loadmodel_sgl.emit()


    # Cycle monitoring model file changes
    def ModelBoxRefre(self):
        pt_list = os.listdir('./models')
        pt_list = [file for file in pt_list if file.endswith('.pt')]
        pt_list.sort(key=lambda x: os.path.getsize('./models/' + x))
        # It must be sorted before comparing, otherwise the list will be refreshed all the time
        if pt_list != self.pt_list:
            self.pt_list = pt_list
            self.model_box.clear()
            self.model_box.addItems(self.pt_list)

    # Get the mouse position (used to hold down the title bar and drag the window)
    def mousePressEvent(self, event):
        p = event.globalPosition()
        globalPos = p.toPoint()
        self.dragPos = globalPos

    # Optimize the adjustment when dragging the bottom and right edges of the window size
    def resizeEvent(self, event):
        # Update Size Grips
        UIFuncitons.resize_grips(self)

    # Exit Exit thread, save settings
    def closeEvent(self, event):
        config_file = 'config/setting.json'
        config = dict()
        config['iou'] = self.iou_spinbox.value()
        config['conf'] = self.conf_spinbox.value()
        config['rate'] = self.speed_spinbox.value()
        config['save_res'] = (0 if self.save_res_button.checkState()==Qt.Unchecked else 2)
        config['save_txt'] = (0 if self.save_txt_button.checkState()==Qt.Unchecked else 2)
        config_json = json.dumps(config, ensure_ascii=False, indent=2)
        with open(config_file, 'w', encoding='utf-8') as f:
            f.write(config_json)
        # Exit the process before closing
        if self.yolo_thread.isRunning():
            self.yolo_predict.stop_dtc = True
            self.yolo_thread.quit()
            MessageBox(
                self.close_button, title='Note', text='Exiting, please wait...', time=3000, auto=True).exec()
            sys.exit(0)
        else:
            sys.exit(0)


if __name__ == "__main__":
    app = QApplication(sys.argv)
    Home = MainWindow()
    Home.show()
    sys.exit(app.exec())  

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