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一、前言
系统介绍
本系统为《基于深度学习的交通场景多目标检测与交通流量分析》，面向交通场景下的车辆、行人等多类目标，构建从识别到统计再到管理的完整闭环。系统首页对识别数据与流量统计结果进行汇总展示；交通识别模块作为核心入口，支持图片识别、视频识别、摄像头识别三种方式，后端统一调用深度学习目标检测模型完成多目标定位与分类，并对视频与摄像头流按帧进行检测与目标跟踪，输出目标类别、位置与数量信息；在检测结果基础上开展交通流量分析，按时间维度统计车流数量与变化趋势，形成可查看的流量统计结果；历史管理模块保存每次识别的记录与统计结果，支持查询与回溯；用户管理模块负责账号信息与登录状态维护。系统采用 Python 语言与 Django 框架搭建后端服务，深度学习部分承担目标检测与多目标跟踪任务，整体功能围绕交通场景识别与流量分析展开，结构清晰、模块职责明确。
选题背景
随着城市车辆数量持续增长，路口和路段的交通状况越来越复杂，单靠人工看监控、手工计数的方式已经很难满足日常管理需要。摄像头虽然铺得不少，但大多数场景下视频数据只是被存下来，真正拿来分析的不多，车流有多少、什么时候多、哪些目标在移动，往往要靠人盯着画面去判断，费时也容易出错。深度学习这几年在目标检测方向进展比较快，能够在画面里同时找出多个车辆、行人等目标并给出位置和类别，这给交通场景的自动识别提供了可行路子。把检测结果进一步做数量统计和时间段分析，就能得到比较直观的交通流量信息。对计算机专业的学生来说，这类题目既有明确的算法落点，又能落到一个可运行的系统上，适合作为毕业设计来做，也能把课堂上学到的深度学习知识真正用起来。
选题意义
这个课题的意义更多体现在实际动手和系统落地这一层。从应用角度看，系统把图片、视频、摄像头三种识别方式统一起来，检测完还能接着做流量统计，比单纯做一个检测 demo 更接近真实使用场景，对路口车流观察、路段目标统计这类需求有一定的参考价值。从学习角度看，做这个系统需要把深度学习模型调用、目标跟踪、后端接口和前端展示串起来，能让学生真正理解一个完整项目是怎么从模型走到功能的，而不是只停留在跑通一个脚本。从能力培养角度看，图片和视频的差异处理、摄像头流的按帧检测、历史记录的保存与查询，这些都需要自己去设计和调试，过程里会遇到不少具体问题，解决这些问题本身就是一种锻炼。整体来说，它不追求多高的技术指标，更看重把一个交通识别与流量分析的小系统做完整、做清楚，作为本科毕设是比较合适的。
二、系统界面展示
- 基于深度学习的交通场景多目标检测与交通流量分析界面展示：
三、代码参考
- 项目实战代码参考：
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("TrafficFlowAnalysis").master("local[*]").getOrCreate()
import os, uuid, datetime
import cv2
import numpy as np
from django.conf import settings
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from .models import DetectHistory, FlowStat, UserInfo
from .detector import YoloDetector
from .tracker import SimpleTracker
detector = YoloDetector(settings.MODEL_PATH)
tracker = SimpleTracker()
def save_history(user_id, source_type, source_path, result_json, target_count):
history = DetectHistory()
history.user_id = user_id
history.source_type = source_type
history.source_path = source_path
history.result_json = result_json
history.target_count = target_count
history.create_time = datetime.datetime.now()
history.save()
return history.id
def analyze_flow_by_spark(records):
rows = []
for item in records:
rows.append((item["hour"], item["vehicle_count"], item["person_count"]))
df = spark.createDataFrame(rows, ["hour", "vehicle_count", "person_count"])
df.createOrReplaceTempView("traffic_flow")
result = spark.sql("select hour, sum(vehicle_count) as vehicle_total, sum(person_count) as person_total from traffic_flow group by hour order by hour")
return [row.asDict() for row in result.collect()]
@csrf_exempt
def image_detect(request):
if request.method != "POST":
return JsonResponse({"code": 400, "msg": "请求方式错误"})
user_id = request.POST.get("user_id")
image_file = request.FILES.get("image")
if not image_file or not user_id:
return JsonResponse({"code": 400, "msg": "参数不完整"})
file_name = str(uuid.uuid4()) + os.path.splitext(image_file.name)[-1]
save_dir = os.path.join(settings.MEDIA_ROOT, "images")
if not os.path.exists(save_dir):
os.makedirs(save_dir)
file_path = os.path.join(save_dir, file_name)
with open(file_path, "wb") as f:
for chunk in image_file.chunks():
f.write(chunk)
image = cv2.imread(file_path)
if image is None:
return JsonResponse({"code": 500, "msg": "图片读取失败"})
detections = detector.detect(image)
vehicle_count = 0
person_count = 0
result_list = []
for det in detections:
label = det["label"]
if label in ["car", "bus", "truck", "motorcycle"]:
vehicle_count += 1
if label == "person":
person_count += 1
result_list.append({"label": label, "bbox": det["bbox"], "score": round(float(det["score"]), 4)})
result_json = {"detections": result_list, "vehicle_count": vehicle_count, "person_count": person_count}
history_id = save_history(user_id, "image", file_path, str(result_json), len(result_list))
return JsonResponse({"code": 200, "msg": "识别成功", "history_id": history_id, "data": result_json})
@csrf_exempt
def video_detect(request):
if request.method != "POST":
return JsonResponse({"code": 400, "msg": "请求方式错误"})
user_id = request.POST.get("user_id")
video_file = request.FILES.get("video")
if not video_file or not user_id:
return JsonResponse({"code": 400, "msg": "参数不完整"})
file_name = str(uuid.uuid4()) + os.path.splitext(video_file.name)[-1]
save_dir = os.path.join(settings.MEDIA_ROOT, "videos")
if not os.path.exists(save_dir):
os.makedirs(save_dir)
file_path = os.path.join(save_dir, file_name)
with open(file_path, "wb") as f:
for chunk in video_file.chunks():
f.write(chunk)
cap = cv2.VideoCapture(file_path)
if not cap.isOpened():
return JsonResponse({"code": 500, "msg": "视频打开失败"})
frame_index = 0
frame_step = 5
hour_records = {}
max_vehicle = 0
max_person = 0
while True:
ret, frame = cap.read()
if not ret:
break
frame_index += 1
if frame_index % frame_step != 0:
continue
detections = detector.detect(frame)
tracks = tracker.update(detections, frame)
vehicle_count = 0
person_count = 0
for track in tracks:
if track["label"] in ["car", "bus", "truck", "motorcycle"]:
vehicle_count += 1
if track["label"] == "person":
person_count += 1
max_vehicle = max(max_vehicle, vehicle_count)
max_person = max(max_person, person_count)
hour_key = str(datetime.datetime.now().hour)
if hour_key not in hour_records:
hour_records[hour_key] = {"hour": hour_key, "vehicle_count": 0, "person_count": 0}
hour_records[hour_key]["vehicle_count"] += vehicle_count
hour_records[hour_key]["person_count"] += person_count
cap.release()
record_list = list(hour_records.values())
flow_result = analyze_flow_by_spark(record_list)
for item in flow_result:
stat = FlowStat()
stat.user_id = user_id
stat.hour = item["hour"]
stat.vehicle_total = item["vehicle_total"]
stat.person_total = item["person_total"]
stat.create_time = datetime.datetime.now()
stat.save()
result_json = {"max_vehicle": max_vehicle, "max_person": max_person, "flow": flow_result}
history_id = save_history(user_id, "video", file_path, str(result_json), max_vehicle + max_person)
return JsonResponse({"code": 200, "msg": "视频识别完成", "history_id": history_id, "data": result_json})
@csrf_exempt
def camera_detect(request):
if request.method != "POST":
return JsonResponse({"code": 400, "msg": "请求方式错误"})
user_id = request.POST.get("user_id")
camera_index = request.POST.get("camera_index", "0")
if not user_id:
return JsonResponse({"code": 400, "msg": "参数不完整"})
cap = cv2.VideoCapture(int(camera_index))
if not cap.isOpened():
return JsonResponse({"code": 500, "msg": "摄像头打开失败"})
frame_count = 0
vehicle_total = 0
person_total = 0
frame_limit = 100
while frame_count < frame_limit:
ret, frame = cap.read()
if not ret:
break
frame_count += 1
if frame_count % 5 != 0:
continue
detections = detector.detect(frame)
tracks = tracker.update(detections, frame)
for track in tracks:
if track["label"] in ["car", "bus", "truck", "motorcycle"]:
vehicle_total += 1
if track["label"] == "person":
person_total += 1
cap.release()
hour_key = str(datetime.datetime.now().hour)
record_list = [{"hour": hour_key, "vehicle_count": vehicle_total, "person_count": person_total}]
flow_result = analyze_flow_by_spark(record_list)
for item in flow_result:
stat = FlowStat()
stat.user_id = user_id
stat.hour = item["hour"]
stat.vehicle_total = item["vehicle_total"]
stat.person_total = item["person_total"]
stat.create_time = datetime.datetime.now()
stat.save()
result_json = {"vehicle_total": vehicle_total, "person_total": person_total, "flow": flow_result}
history_id = save_history(user_id, "camera", "camera_" + camera_index, str(result_json), vehicle_total + person_total)
return JsonResponse({"code": 200, "msg": "摄像头识别完成", "history_id": history_id, "data": result_json})
四、系统视频
基于深度学习的交通场景多目标检测与交通流量分析项目视频：
演示视频
结语
最新计算机毕业设计选题推荐-基于深度学习的交通场景多目标检测与交通流量分析-运行部署-文档指导-深度学习-YOLO
想看其他类型的计算机毕业设计作品也可以和我说都有 谢谢大家！
有技术这一块问题大家可以评论区交流或者私我~
大家可以帮忙点赞、收藏、关注、评论啦～
源码获取：⬇⬇⬇
转载自 CSDN-专业IT技术社区
原文链接：https://blog.csdn.net/2301_79456892/article/details/168870548
