智能识别行人横穿马路算法模型的图像训练数据

本数据集主要用于提升AI模型对行人违规横穿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精准识别行人未走斑马线、闯红灯、翻越护栏等危险行为，并可应用于城市道路、校园周边等重点区域的智能监控场景。同时，本数据集可为交警非现场执法提供证据支持，并为危险路段的安全改造提供数据依据，有效提升道路交通安全管理水平。
1.数据采集
通过企业自有摄像设备自行采集道路图像，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、车道类型等数据。
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：合规通行/违规横穿
二级标签：未走斑马线/闯红灯/翻越护栏/其他
​辅助标注：行人边界框坐标
3.模型选择与初始化
采用YOLOv8-Pose+DeepSORT多目标跟踪模型架构，初始化参数并优化超参数：学习率0.001-0.0001动态调整，批量大小1-32动态调整，锚框参数调整适配路人特征，集成姿态估计模块提升动作识别准确率。
4.模型训练
基于PyTorch实施分布式训练，采用混合精度训练（FP16）提升效率。设置训练时长，数据增强模拟复杂场景，添加动态模糊、遮挡物干扰等特效，模拟夜间低光照及雨雾天气条件。设置早停机制（patience=20），梯度裁剪：max_norm=1.0。
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：逆光场景检出率

This dataset is primarily developed to improve the recognition accuracy and capability of AI models for detecting pedestrians' illegal jaywalking behaviors. Trained using this dataset, AI models can accurately identify dangerous pedestrian behaviors including failing to use crosswalks, running red lights, climbing guardrails, etc. through image analysis, and can be applied to intelligent monitoring scenarios in key areas such as urban roads and school peripheries. Meanwhile, this dataset can provide evidentiary support for traffic police's off-site law enforcement and serve as a data basis for safety renovation of high-risk road sections, effectively elevating the level of road traffic safety management.
1. Data Collection
Road images are collected with the enterprise's proprietary camera equipment, while supporting data such as image ID, collection timestamp, device model, geographic coordinates, lighting conditions, weather status, and lane type are synchronously recorded.
2. Data Preprocessing and Annotation
Blurry and duplicate images are removed via data cleaning. The dataset is split into training, validation, and test sets at a ratio of 6:2:2. A multi-level annotation framework is established:
Level 1 labels: Legal Crossing / Illegal Jaywalking
Level 2 labels: Failing to Use Crosswalks / Running Red Lights / Climbing Guardrails / Others
Auxiliary Annotation: Pedestrian Bounding Box Coordinates
3. Model Selection and Initialization
The YOLOv8-Pose + DeepSORT multi-object tracking model architecture is adopted, with parameter initialization and hyperparameter optimization conducted as follows: dynamically adjust the learning rate within the range of 0.001 to 0.0001, dynamically adjust the batch size between 1 and 32, optimize anchor box parameters to adapt to pedestrian features, and integrate a pose estimation module to improve the accuracy of action recognition.
4. Model Training
Distributed training is implemented based on PyTorch, and mixed-precision training (FP16) is adopted to enhance training efficiency. Training duration is set, and data augmentation is utilized to simulate complex scenarios, adding effects such as dynamic blur and occlusion interference to replicate low-light nighttime and rainy/foggy weather conditions. An early stopping mechanism (patience=20) and gradient clipping (max_norm=1.0) are configured.
5. Model Evaluation
During the model training process, the validation set is used to adjust hyperparameters. After training is completed, the model's performance is evaluated on the test set, with evaluation metrics including:
Basic Performance: mAP@0.5, False Positive Rate
Scenario Robustness Test: Detection Rate in Backlight Scenarios

搜集汇总

背景与挑战

背景概述

该数据集是用于训练智能识别行人横穿马路算法模型的图像数据，包含592条xlsx格式记录，每日更新，通过企业自有设备采集道路图像并标注行人违规行为（如未走斑马线、闯红灯）。其特点在于采用YOLOv8-Pose+DeepSORT模型架构进行训练和评估，旨在提升AI模型在复杂场景下的识别精度，应用于城市监控和交通安全管理，支持非现场执法和路段改造。