智能识别非法小广告张贴（墙面/护栏等）算法模型的图像训练数据

本数据集主要用于提升AI模型对城市公共区域非法小广告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检测电线杆、护栏、墙面等公共设施上的违规张贴物，并可应用于城市市容管理、社区环境维护、市政设施维护及智慧城市治理等场景。同时，本数据集可为城管部门提供智能化执法依据，提升城市形象管理效率；为社区物业提供实时监控支持，维护居民生活环境整洁；为市政设施维护评估提供数据支撑；为智慧城市综合治理平台建设提供技术支持，从而全面提升城市管理效率和市容环境质量。1.数据采集
通过企业自有摄像设备自行采集道路小广告张贴区域图像，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、过曝或严重遮挡图像。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/违规
二级标签：纸质广告/喷涂广告/其他
辅助标注广告边界框坐标、设施类型（电线杆/护栏/墙面等）。
3.模型选择与初始化
采用YOLOv8s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，锚框参数适配小广告形态；集成OCR模块用于内容识别。
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，数据增强模拟不同张贴角度（倾斜、褶皱），添加光影变化、部分遮挡，模拟雨雾场景、老化褪色效果等特效，设置早停机制（patience=10）和梯度裁剪（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：单一广告→密集广告，完整广告→破损广告

This dataset is mainly used to improve the recognition capability and accuracy of AI models for illegal small advertisements in urban public areas. Trained on this dataset, AI models can accurately detect illegally posted materials on public facilities such as utility poles, guardrails and walls through image analysis, and can be applied to scenarios including urban city appearance management, community environmental maintenance, municipal facility maintenance and smart city governance. Meanwhile, this dataset can provide intelligent law enforcement basis for urban management departments to improve the efficiency of urban image management; offer real-time monitoring support for community properties to maintain the cleanliness of residents' living environments; provide data support for municipal facility maintenance evaluation; and offer technical support for the construction of smart city comprehensive management platforms, thereby comprehensively enhancing urban management efficiency and the quality of urban appearance and environment.
1. Data Collection
Collect images of areas with posted small advertisements on roads using the enterprise's own camera equipment, and synchronously record data such as image ID, collection time, equipment model, geographic coordinates, lighting conditions and weather conditions.
2. Data Preprocessing and Annotation
Eliminate blurry, overexposed or severely occluded images via data cleaning. Divide the dataset into training set, validation set and test set at a ratio of 6:2:2. Establish a multi-level annotation system:
First-level label: Compliant / Violating
Second-level label: Paper advertisement / Spray-painted advertisement / Others
Auxiliary annotations include advertising bounding box coordinates and facility type (utility pole / guardrail / wall, etc.).
3. Model Selection and Initialization
Adopt the pre-trained YOLOv8s model, initialize parameters and optimize hyperparameters: dynamically adjust the learning rate within the range of 0.001 to 0.0001, dynamically adjust the batch size from 1 to 32, and adapt anchor box parameters to the shape of small advertisements; integrate an OCR module for content recognition.
4. Model Training
Implement distributed training based on PyTorch, and use mixed-precision training (FP16) to improve training efficiency. Set the training duration, apply data augmentation to simulate different posting angles (tilted, wrinkled), add light and shadow changes and partial occlusion, simulate rain and fog scenarios, aging and fading effects and other special effects, and set an early stopping mechanism (patience=10) and gradient clipping (max_norm=1.0).
5. Model Evaluation
During the model training process, use the validation set to adjust hyperparameters. After the training is completed, evaluate the model performance on the test set. The evaluation indicators include:
Basic performance indicators: mAP@0.5, false positive rate
Scene robustness test: detection rate in rainy and foggy weather
Progressive testing is also set up: single advertisement → dense advertisement, complete advertisement → damaged advertisement

搜集汇总

背景与挑战

背景概述

该数据集是用于训练AI模型识别城市公共区域非法小广告的图像数据，包含584条xlsx格式记录，每日更新；应用场景包括城市市容管理和智慧城市治理，通过YOLOv8s算法进行模型训练，提升检测精度和鲁棒性。