智能识别动物闯入道路（如流浪狗等）算法模型的图像训练数据

本数据集主要用于提升AI模型对道路上动物闯入行为的识别能力与精确性。通过对该数据集的训练，使AI模型能够通过图像识别技术检测犬类猫类等动物闯入道路的情况，并可应用于城市道路、高速公路等场景的智能监控系统。同时，本数据集可为交通管理部门提供实时预警信息，有效降低因动物闯入引发的交通事故风险，提升道路安全防护水平。
1.数据采集
通过企业自有摄像设备自行采集道路路面动物图像，同步记录图像ID、采集时间、设备型号、地理坐标、光照强度、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划分训练集/验证集/测试集。设置多级标注体系：
​一级标签：动物出现/无动物
​二级标签：犬类/猫类/其他
三级标签：静止/奔跑/横穿道路
​辅助标注：动物边界框坐标
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1-64动态调整，锚框参数适配中小型动物形态；集成SE注意力机制提升小目标检测能力。
4.模型训练
基于PyTorch实施两阶段训练，采用混合精度训练（FP16）提升效率，设置训练时长，数据增强模拟雨雾、车灯眩光等干扰，设置早停机制（patience=10）并采用Focal Loss（α=0.8, γ=2）优化困难样本。
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：夜间检测率
并设置渐进式测试：静态动物→动态奔跑、单一目标→多目标交互、白天→夜间/雨雾场景。

This dataset is primarily developed to improve the recognition capability and accuracy of AI models in detecting animal intrusions on roads. Training on this dataset enables AI models to detect scenarios where animals such as dogs and cats intrude onto roads via image recognition technology, and can be applied to intelligent monitoring systems in scenarios like urban roads and highways. Meanwhile, this dataset can provide real-time early warning information for traffic management departments, effectively reducing the risk of traffic accidents caused by animal intrusions and improving road safety protection levels.
1. Data Collection
Images of animals on road surfaces are collected independently using the enterprise's own camera equipment, with associated data including image ID, collection time, device model, geographic coordinates, light intensity, weather conditions and other information recorded synchronously.
2. Data Preprocessing and Annotation
Blurry and duplicate images are eliminated 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 label: Animal present / No animal
Secondary label: Dog / Cat / Others
Tertiary label: Stationary / Running / Crossing the road
Auxiliary annotation: Animal bounding box coordinates
3. Model Selection and Initialization
The pre-trained YOLOv8s model is adopted, with initialization parameters and optimized hyperparameters: dynamically adjusted learning rate ranging from 0.001 to 0.0001, dynamically adjusted batch size ranging from 1 to 64, anchor box parameters adapted to the morphology of small and medium-sized animals; the SE attention mechanism is integrated to enhance the detection capability for small targets.
4. Model Training
Two-stage training is implemented based on PyTorch, with mixed-precision training (FP16) adopted to improve efficiency. Training duration is set, data augmentation is used to simulate interferences such as rain, fog, and vehicle light glare, an early stopping mechanism (patience=10) is configured, and Focal Loss (α=0.8, γ=2) is used to optimize hard samples.
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
Scenario robustness test: Nighttime detection rate
Progressive testing is also set up: Static animals → Dynamic running, Single target → Multi-target interaction, Daytime → Nighttime/rainy and foggy 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动物闯入道路算法模型的图像数据，包含559条企业自行采集的xlsx格式数据，每日更新。它通过多级标注和YOLOv8s模型优化，专注于检测犬类、猫类等动物在道路上的行为，应用于智能监控系统以提升道路安全，降低交通事故风险。