智能识别路边乞讨/发广告影响交通算法模型的图像训练数据
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本数据集主要用于提升AI模型对路边乞讨、发放广告等影响交通秩序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乞讨、广告派发行为特征，并可应用于城市管理、交通执法及智慧警务等场景。同时，本数据集可为城市管理部门提供智能化监管手段，有效减少因违规行为导致的交通拥堵和安全隐患，为文明城市创建提供技术支持，并显著降低人工巡查成本，提升城市精细化管理水平。 1.数据采集 通过企业自有摄像设备自行采集道路乞讨、发广告人员图像，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：合规行为/违规行为 二级标签：乞讨行为/发广告行为/其他违规 辅助标注：人员边界框坐标、影响区域坐标。 3.模型选择与初始化 采用YOLOv8预训练模型，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，锚框参数适配人体姿态和小型广告牌；集成注意力机制提升小目标识别能力。 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、强光反射（广告牌反光）、局部遮挡（行人/车辆遮挡）等特效。设置早停机制（patience=15），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： 基础性能指标：mAP@0.5、误报率 场景鲁棒性测试：雨雾天气检出率 并设置渐进式测试：单人违规→群体违规，静态场景→动态追逐场景。
This dataset is primarily designed to enhance the recognition accuracy and capability of AI models in identifying traffic-disrupting behaviors such as roadside begging and advertisement distribution. Trained on this dataset, AI models can recognize the characteristics of begging and advertisement distribution behaviors via image analysis, and be applied in scenarios like urban management, traffic law enforcement, and smart policing. Meanwhile, this dataset provides intelligent supervision means for urban management authorities, effectively reducing traffic congestion and safety hazards caused by such violations, offering technical support for the establishment of civilized cities, significantly lowering manual inspection costs, and improving the level of refined urban management. 1. Data Collection Images of people conducting roadside begging or advertisement distribution 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, and weather conditions. 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 7:2:1. A multi-level annotation system is established: - Primary labels: Compliant behaviors / Violating behaviors - Secondary labels: Begging behaviors / Advertisement distribution behaviors / Other violations - Auxiliary annotations: Bounding box coordinates of personnel, coordinates of affected areas. 3. Model Selection and Initialization A pre-trained YOLOv8 model is adopted, with parameter initialization and hyperparameter optimization: dynamically adjusting the learning rate within 0.001-0.0001, dynamically adjusting the batch size within 1-32, adapting anchor box parameters to human postures and small billboards; integrating an attention mechanism to improve the recognition capability of small targets. 4. Model Training Distributed training is implemented based on PyTorch, with mixed-precision training (FP16) adopted to improve efficiency. Training duration is set, and data augmentation is used to simulate complex scenarios, adding effects such as motion blur, strong light reflection (billboard reflection), and partial occlusion (obstruction by pedestrians/vehicles). An early stopping mechanism (patience=15) is set, with gradient clipping: max_norm=1.0. 5. Model Evaluation During model training, the validation set is used to adjust hyperparameters. After training is completed, model performance is evaluated on the test set, with evaluation metrics including: - Basic performance metrics: mAP@0.5, false positive rate - Scene robustness test: detection rate in rainy and foggy weather Progressive testing is also set up: single-person violation → group violation, static scenario → dynamic chasing scenario.
