智能识别行人闯红灯算法模型的图像训练数据

本数据集主要用于提升AI模型对交通路口行人闯红灯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识别行人闯红灯的违规行为，并可应用于城市智能交通管理系统、电子警察执法系统及智慧城市安全监控等场景。同时，本数据集可为交通管理部门提供智能化执法依据，有效提升行人交通违法行为的查处效率，为构建安全有序的城市交通环境提供数据支撑。
1.数据采集
通过企业自有摄像设备自行采集道路行人图像，同步记录图像ID、采集时间、设备型号、地理坐标、光照条件、天气状况红绿灯状态（红灯/绿灯/黄灯）等数据。
2.数据预处理与标注
通过数据清洗剔除模糊、重复图像。按7:2:1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/违规
二级标签：单人闯灯/群体闯灯/徘徊闯灯
辅助标注：行人边界框坐标、移动方向
3.模型选择与初始化
采用YOLOv8x+FairMOT多目标跟踪联合框架，初始化参数并优化关键超参数：学习率采用0.01-0.0001动态调整，批量大小1-64动态调整，锚框参数根据行人形态特征定制，FairMOT分支同步配置512维ReID特征提取器，并采用Triplet Loss增强行人重识别能力。
4.模型训练
基于PyTorch实施两阶段分布式训练，采用混合精度训练（FP16）提升计算效率。通过雨雾遮挡模拟、人群密度增强等数据增强手段提升模型鲁棒性，特别针对儿童和群体闯灯场景添加困难样本训练。设置早停机制（patience=25）和梯度裁剪（max_norm=2.0）确保训练稳定性。
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：大雨天气检出率

This dataset is primarily designed to enhance the recognition capability and accuracy of AI models in detecting pedestrian red-light running violations at traffic intersections. Training AI models on this dataset enables them to identify pedestrian red-light running violations through image analysis, and can be applied in scenarios such as urban intelligent traffic management systems, electronic police law enforcement systems, and smart city security monitoring applications. Meanwhile, this dataset can provide intelligent law enforcement basis for traffic management departments, effectively improving the efficiency of investigating and penalizing pedestrian traffic violations, and offering data support for building a safe and orderly urban traffic environment.
1. Data Collection
Road pedestrian images are collected using the enterprise's own camera equipment, with synchronized recording of data including image ID, collection time, device model, geographic coordinates, lighting conditions, weather conditions, and traffic light status (red light/green light/yellow light).
2. Data Preprocessing and Annotation
Blurry and duplicate images are removed via data cleaning. The dataset is split into training/validation/test sets at a ratio of 7:2:1. A multi-level annotation system is established:
- Primary labels: Compliant / Violation
- Secondary labels: Single-person red-light running / Group red-light running / Wandering red-light running
- Auxiliary annotations: Pedestrian bounding box coordinates, movement direction
3. Model Selection and Initialization
A YOLOv8x + FairMOT multi-object tracking joint framework is adopted. The model parameters are initialized and key hyperparameters are optimized: the learning rate is dynamically adjusted within the range of 0.01-0.0001, the batch size is dynamically adjusted within 1-64, the anchor box parameters are customized based on pedestrian morphological features, the FairMOT branch is configured with a 512-dimensional ReID feature extractor, and Triplet Loss is used to enhance pedestrian re-identification capability.
4. Model Training
Two-stage distributed training is implemented based on PyTorch, with mixed-precision training (FP16) adopted to improve computational efficiency. Data augmentation methods such as rain-fog occlusion simulation and crowd density enhancement are used to enhance model robustness, and hard sample training is specifically added for scenarios of children and group red-light running. An early stopping mechanism (patience=25) and gradient clipping (max_norm=2.0) are set to ensure training stability.
5. Model Evaluation
During the model training process, the validation set is used to adjust hyperparameters. After training is completed, the model performance is evaluated on the test set. The evaluation metrics include:
- Basic performance: mAP@0.5, false positive rate
- Scene robustness test: Detection rate under heavy rain weather

搜集汇总

背景与挑战

背景概述

该数据集包含583条图像训练数据，每日更新，用于训练AI模型识别行人闯红灯行为，提升智能交通系统的执法效率。数据通过自有摄像设备采集，采用YOLOv8x和FairMOT框架进行多目标跟踪和模型优化，增强了对复杂场景如群体闯灯和恶劣天气的鲁棒性。