交通AI算法

应对各类智能化交通业务分析需求，使用深度学习框架、图像预处理、数据后处理技术进行应用算法的开发，构建交通AI分析算法库。提供面向城市交通运输、公交、出租、交管、海洋交通领域细分业务的22类分析算法模型，包括：人脸识别检测、人群密度检测、区域入侵检测、烟火检测、行人流量统计及轨迹跟踪、场站人流拥堵预警、公交拥挤度检测、营运车辆运营状态识别、出租车人数检测、工地安全帽检测、工地未穿反光衣检测、路面抛洒物检测、路面病害检测、车辆流量统计及轨迹跟踪、电动车头盔佩戴检测、电动车进电梯检测、车辆属性识别、道路拥堵预警、车牌识别分析、船舶在离港数量统计及轨迹跟踪、船上/渡口人数检测、船上/渡口车辆识别统计。可根据实际业务需求提供对应的算法分析接口。

序号
算法名称
算法简介

1
人脸识别检测
支持人脸轮廓检测、脸部关键点识别、人脸信息验证等功能，可用于营运车辆司机人证合一检测、日常考勤打卡等场景。

2
人群密度检测
基于深度学习算法实现密集人群的人头检测，能够对特定场所的人群密度进行识别，并根据设定阈值及布防条件进行告警。

3
区域入侵检测
利用神经网络模型，结合向量的特性判断在划定的区域内，是否有特定的目标进入，不同于电子围栏的无差别入侵告警，该功能准确定位入侵目标类型及位置信息，例如针对人、车、物等目标入侵情况进行抓拍告警。

4
烟火检测
利用深度学习模型对明火进行检测，一旦关注区域发生火灾能够及时进行预警。

5
行人流量统计及轨迹跟踪
基于行人目标检测模型及目标跟踪算法对特定场景下的行人轨迹进行跟踪，并统计各个方向的行人流量。

6
场站人流拥堵预警
利用行人检测模型，实时监测场站的人流情况，一旦排队长度超过阈值就产生拥堵预警并上报中心，及时进行疏导。

7
公交拥挤度检测
利用目标分类模型，对公交/BRT车厢里内的各个位置，车头、车尾及上下客区域进行拥挤分类，最终根据加权计算结果判断整个车厢的拥挤程度，共分为5个等级，分别是拥挤、轻度拥挤、适中、舒适、稀疏等。

8
营运车辆运营状态识别
利用营运车辆的车内监控图片，采用深度学习模型识别车内状态，区分车厢有人、车厢没人、其他（包括驾驶座、车外、上车门）三种状态。

9
出租车人数检测
利用出租车内抓拍的图片，采用人数检测模型识别车内载客人数情况。

10
工地安全帽检测
利用目标检测模型对施工区域作业人员，或其他特殊工种未戴安全帽的情况进行识别及预警。

11
工地未穿反光衣检测
利用目标检测模型对施工区域作业人员，或其他特殊工种未穿反光衣的情况进行识别及预警。

12
路面抛洒物检测
利用目标检测模型及图像仿射变换算法，过滤掉干扰区域，从而实现对路面抛洒物的自动化检测，并记录位置信息生成结构化数据。方便道路日常巡检维护。

13
路面病害检测
利用深度学习目标检测算法，利用车载前景摄像机、无人机或道路高清摄像实现对路面龟裂、凹陷、坑槽等病害的自动化检测，并记录位置信息，生成结构化数据。方便道路日常巡检维护。

14
车辆流量统计及轨迹跟踪
基于车辆目标检测模型及轻量化目标跟踪算法对特定场景下的车辆轨迹进行跟踪，并统计各个方向的车流量。

15
电动车头盔佩戴检测
利用道路高清摄像头，实时抓拍电动车驾驶员照片，基于电动车与安全帽检测模型，对电动车驾驶员未佩戴头盔的情况进行检测。

16
电动车进电梯检测
基于电动车与区域入侵检测模型，对电动车进电梯的情况进行识别，并能够及时产生预警。

17
车辆属性识别
利用细粒度目标属性提取模型，结合车辆检测模型提取个每辆车的ROI区，从而实现能对车辆属性，如车辆类型、颜色等进行识别。

18
道路拥堵预警
基于车辆检测及轨迹跟踪模型，获取道路实时车流量、车头时距、道路密度信息，结合交通流理论构建道路拥堵判别模型，对道路交通流状态实时感知，一旦发生拥堵及时产生告警并上报指挥中心。

19
车牌识别分析
利用HyperLPR算法，对特定角度下的高清摄像头抓拍到的车牌进行识别，支持蓝牌、绿牌及黄牌牌照识别。

20
船舶在离港数量统计及轨迹跟踪
基于船舶检测模型及轻量化目标跟踪算法对渔港、码头等区域的船舶轨迹进行跟踪，实时统计在离港船舶数量。适用于对休渔期渔船非法出海捕捞抓拍，及台风天渔港情况监测等场景。

21
船上/渡口人数检测
利用船舶检测模型或区域入侵判断算法，明确检测区域，再结合行人检测模型能够对船上/渡口的人员情况进行检测。

22
船上/渡口车辆识别统计
利用船舶检测模型或区域入侵判断算法，明确检测区域，再结合车辆目标检测模型能够对船上/渡口的车辆数况进行检测。

To meet the needs of various intelligent transportation business analysis, application algorithms are developed using deep learning frameworks, image preprocessing, and data post-processing technologies to build a traffic AI analysis algorithm library. A total of 22 types of analysis algorithm models are provided for segmented businesses in urban transportation, public transit, taxis, traffic management, and maritime transportation, including: Face Recognition Detection, Crowd Density Detection, Area Intrusion Detection, Fire and Smoke Detection, Pedestrian Flow Statistics and Trajectory Tracking, Station Passenger Congestion Alert, Bus Congestion Detection, Operating Vehicle Operation Status Recognition, Taxi Passenger Count Detection, Construction Site Helmet Detection, Construction Site Reflective Clothing Non-wearing Detection, Road Surface Debris Detection, Road Surface Disease Detection, Vehicle Flow Statistics and Trajectory Tracking, Electric Vehicle Helmet Wearing Detection, Electric Vehicle Elevator Entry Detection, Vehicle Attribute Recognition, Road Congestion Alert, License Plate Recognition Analysis, Ship Departure/Arrival Quantity Statistics and Trajectory Tracking, On-board/Ferry Terminal Passenger Detection, On-board/Ferry Terminal Vehicle Recognition and Statistics. Corresponding algorithm analysis interfaces can be provided according to actual business requirements.

Serial Number Algorithm Name Algorithm Introduction

1 Face Recognition Detection Supports functions such as facial contour detection, facial key point recognition, and facial information verification, which can be used for scenarios like driver identity verification for operating vehicles and daily attendance checking.

2 Crowd Density Detection Implements head detection for dense crowds based on deep learning algorithms, can identify the crowd density in specific places, and sends alerts according to set thresholds and defense deployment conditions.

3 Area Intrusion Detection Uses neural network models combined with vector characteristics to determine whether specific targets enter the designated area. Unlike the undifferentiated intrusion alert of electronic fences, this function accurately locates the type and location of intrusion targets, such as capturing and alerting for target intrusions of people, vehicles, and objects.

4 Fire and Smoke Detection Uses deep learning models to detect open flames, and can issue timely early warnings once a fire occurs in the concerned area.

5 Pedestrian Flow Statistics and Trajectory Tracking Tracks pedestrian trajectories in specific scenarios based on pedestrian object detection models and object tracking algorithms, and counts pedestrian flow in all directions.

6 Station Passenger Congestion Alert Uses pedestrian detection models to monitor the passenger flow situation at stations in real time, and generates congestion alerts and reports to the central command once the queue length exceeds the threshold to facilitate timely traffic diversion.

7 Bus Congestion Detection Uses object classification models to classify the congestion levels of various positions in bus/BRT carriages, including the front, rear, and passenger boarding/alighting areas. Finally, the congestion level of the entire carriage is judged based on weighted calculation results, which are divided into 5 levels: crowded, mildly crowded, moderate, comfortable, and sparse.

8 Operating Vehicle Operation Status Recognition Uses in-vehicle surveillance images of operating vehicles and deep learning models to identify the in-vehicle status, distinguishing three states: occupied, unoccupied, and others (including driver's seat, exterior of the vehicle, and boarding door).

9 Taxi Passenger Count Detection Uses images captured inside taxis and passenger count detection models to identify the number of passengers in the taxi.

10 Construction Site Helmet Detection Uses object detection models to identify and alert workers in construction areas or other special types of workers who are not wearing helmets.

11 Construction Site Reflective Clothing Non-wearing Detection Uses object detection models to identify and alert workers in construction areas or other special types of workers who are not wearing reflective clothing.

12 Road Surface Debris Detection Uses object detection models and image affine transformation algorithms to filter out interference areas, realizing automatic detection of road surface debris, recording location information and generating structured data, which facilitates daily road inspection and maintenance.

13 Road Surface Disease Detection Uses deep learning object detection algorithms, combined with vehicle-mounted forward-looking cameras, unmanned aerial vehicles (UAVs) or road high-definition cameras to automatically detect road surface diseases such as cracking, depressions, and potholes, record location information and generate structured data, which facilitates daily road inspection and maintenance.

14 Vehicle Flow Statistics and Trajectory Tracking Tracks vehicle trajectories in specific scenarios based on vehicle object detection models and lightweight object tracking algorithms, and counts vehicle flow in all directions.

15 Electric Vehicle Helmet Wearing Detection Uses road high-definition cameras to capture real-time photos of electric vehicle drivers, and detects situations where electric vehicle drivers are not wearing helmets based on electric vehicle and helmet detection models.

16 Electric Vehicle Elevator Entry Detection Uses electric vehicle and area intrusion detection models to identify situations where electric vehicles enter elevators, and can issue timely alerts.

17 Vehicle Attribute Recognition Uses fine-grained object attribute extraction models combined with vehicle detection models to extract the Region of Interest (ROI) area of each vehicle, thereby realizing recognition of vehicle attributes such as vehicle type and color.

18 Road Congestion Alert Based on vehicle detection and trajectory tracking models, obtains real-time vehicle flow, headway, and road density information, constructs a road congestion discrimination model combined with traffic flow theory, realizes real-time perception of road traffic flow status, and issues timely alerts and reports to the command center once congestion occurs.

19 License Plate Recognition Analysis Uses the HyperLPR algorithm to recognize license plates captured by high-definition cameras at specific angles, and supports recognition of blue, green, and yellow license plates.

20 Ship Departure/Arrival Quantity Statistics and Trajectory Tracking Tracks ship trajectories in areas such as fishing ports and wharves based on ship detection models and lightweight object tracking algorithms, and counts the number of ships departing/anchored in real time. It is applicable to scenarios such as capturing illegal offshore fishing by fishing boats during the fishing moratorium and monitoring fishing port conditions during typhoon days.

21 On-board/Ferry Terminal Passenger Detection Defines the detection area using ship detection models or area intrusion judgment algorithms, and then combines with pedestrian detection models to detect the passenger situation on board or at ferry terminals.

22 On-board/Ferry Terminal Vehicle Recognition and Statistics Defines the detection area using ship detection models or area intrusion judgment algorithms, and then combines with vehicle object detection models to detect and count the number of vehicles on board or at ferry terminals.

搜集汇总

背景与挑战

背景概述

该数据集是一个交通AI算法库，专注于使用深度学习技术开发22类分析算法模型，以支持城市交通运输、公交、出租、交管和海洋交通等领域的智能化业务需求。这些算法涵盖人脸识别、人群密度检测、车辆跟踪、路面病害检测等多种功能，旨在通过具体的算法接口应对实际交通场景中的分析挑战。