智能识别压线行驶（实线变道）算法模型的图像训练数据

本数据集主要用于提升AI模型对车辆违规压线（实线变道）行为的识别能力与精确性。通过对该数据集的训练，使AI模型能够通过图像分析判断车辆轨迹与车道线位置关系，并可应用于高速公路、城市道路、隧道等场景的智能交通执法系统。同时，本数据集可为电子警察设备提供智能化违规取证支持，实现交通违法行为的自动识别与记录，有效提升交通执法效率与道路安全水平。
1. 数据采集
通过企业自有摄像设备自行采集道路车辆图像，同步记录图像ID、采集时间、设备型号、地理坐标、光照条件、天气状况、车道线类型等数据。
2. 数据预处理与标注​​
通过数据清洗，剔除镜头遮挡、极端低照度导致的无效帧。按6:2:2比例划分训练集/验证集/测试集。设置多级标注体系：
​一级标签：合规行驶/压线行驶
​二级标签：实线变道/虚线区域压线/导流线违规压线
​辅助标注：车辆边界框坐标
3.模型选择与初始化
采用YOLOv8+DeepSORT多目标跟踪框架，骨干网络CSPDarknet53，初始化参数并优化超参数：学习率0.01-0.001余弦退火调整，批量大小1-32动态调整，锚框参数适配适配常见车辆形态。
4. 模型训练
采用YOLOv8+DeepSORT+LSTM两阶段训练策略，设置训练时长。数据增强模拟雨天反光，添加运动模糊，阴影干扰等。设置早停机制（patience=12），梯度裁剪（max_norm=2.0）防过拟合。
5. 模型评估
在训练模型的过程中，使用验证集调整超参数，训练完成后在测试集上评估模型表现，评估指标包含：
基础性能：mAP@0.5、误报率
场景鲁棒性测试：暴雨天气检出率

This dataset is primarily developed to improve the recognition ability and accuracy of AI models in identifying vehicles that violate traffic regulations by crossing lane lines (specifically, lane-changing across solid lines). Training with this dataset enables AI models to analyze images to judge the positional relationship between vehicle trajectories and lane lines, and can be applied to intelligent traffic law enforcement systems in scenarios such as expressways, urban roads, and tunnels. Moreover, this dataset can provide intelligent violation evidence collection support for electronic police equipment, realizing automatic identification and recording of traffic violations, and effectively enhancing traffic law enforcement efficiency and road safety level.
1. Data Collection
Road vehicle images are collected independently using the enterprise's own camera equipment, with synchronized recording of data including image ID, collection time, equipment model, geographic coordinates, lighting conditions, weather conditions, lane line types and other related information.
2. Data Preprocessing and Annotation
Invalid frames caused by lens occlusion or extremely low illumination are eliminated through data cleaning. The dataset is divided into training set, validation set and test set at a ratio of 6:2:2. A multi-level annotation system is set up:
- Primary label: Compliant driving / Lane line crossing violation
- Secondary label: Changing lanes across solid lines / Lane crossing in dashed line areas / Violation of lane line crossing on guide lines
- Auxiliary annotation: Vehicle bounding box coordinates
3. Model Selection and Initialization
The YOLOv8+DeepSORT multi-object tracking framework is adopted, with the backbone network CSPDarknet53. The initialization parameters are set and hyperparameters are optimized: cosine annealing adjustment of learning rate from 0.01 to 0.001, dynamic adjustment of batch size from 1 to 32, and anchor box parameters adapted to common vehicle shapes.
4. Model Training
A two-stage training strategy of YOLOv8+DeepSORT+LSTM is adopted, with the training duration set. Data augmentation is conducted to simulate rain reflection, motion blur, shadow interference and other scenarios. An early stopping mechanism (patience=12) and gradient clipping (max_norm=2.0) are set to prevent overfitting.
5. Model Evaluation
During the model training process, the validation set is used to adjust hyperparameters. After the training is completed, the model performance is evaluated on the test set. The evaluation metrics include:
- Basic performance: mAP@0.5, false positive rate
- Scenario robustness test: Detection rate in heavy rain weather

搜集汇总

背景与挑战

背景概述

该数据集是用于训练AI模型识别车辆压线行驶（如实线变道）的图像数据，包含621条记录，每日更新，采用xlsx格式。其特点包括多级标注体系（如合规/违规分类）和基于YOLOv8+DeepSORT的算法框架，旨在提升智能交通执法系统在高速公路等场景中的识别精度和效率。