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# **Intersection-Flow-5K: A High-Density Traffic Surveillance Dataset for Object Detection** |
[](https://arxiv.org/abs/2508.19565) |
[](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
Welcome to the official repository for the **Intersection-Flow-5K** dataset, introduced in our paper: |
> **FlowDet: Overcoming Perspective and Scale Challenges in Real-Time End-to-End Traffic Detection** |
> |
> *Yuhang Zhao, Zixing Wang* |
> |
> *PRCV, 2025* |
This dataset is specifically designed to address the unique challenges of real-world, infrastructure-based traffic monitoring. It features high-density scenes, extreme scale variations, and severe, persistent occlusions, providing a challenging benchmark for modern object detectors. |
## **1. Dataset Highlights & Challenges** |
Existing object detection benchmarks often fall short of capturing the complexities of fixed-camera traffic surveillance. Intersection-Flow-5K was created to fill this gap, offering a unique set of challenges: |
* **Extreme Scale Variation**: Objects range from distant vehicles appearing as small as `15x15` pixels to large trucks occupying over `800x600` pixels within a single frame. |
* **High Object Density & Severe Occlusion**: The dataset includes rush-hour scenes with numerous overlapping vehicles, leading to persistent and severe occlusions (up to 75% annotated). |
* **Diverse Environmental Conditions**: Data was collected from 7 distinct urban intersections, covering various times of day (daylight, nighttime with glare) and weather conditions (clear, overcast, rainy). |
* **Comprehensive Annotations**: Meticulously annotated with high-quality bounding boxes for crucial traffic participants. |
This benchmark is ideal for researchers working on robust object detection, small object detection, detection in crowded scenes, and real-time intelligent transportation systems (ITS). |
## **2. Dataset Overview** |
* **Task Type**: Object Detection |
* **Total Images**: 6,928 high-resolution (`1920x1080`) images |
* **Training Set**: 5,483 images (80%) |
* **Validation Set**: 722 images (10%) |
* **Test Set**: 723 images (10%) |
* **Total Annotations**: Over 95,000 bounding boxes |
* **Number of Categories**: 8 |
* **Category List (`classes.txt`)**: |
```txt |
vehicle |
bus |
bicycle |
pedestrian |
engine |
truck |
tricycle |
obstacle |
``` |
## **3. Directory Structure** |
The dataset is organized as follows: |
```bash |
Intersection-Flow-5K/ |
├── images/ # Original high-resolution images |
│ ├── train/ # 5,483 images |
│ ├── val/ # 722 images |
│ └── test/ # 723 images |
│ |
├── labels/ # Annotations in YOLO .txt format |
│ ├── train/ |
│ ├── val/ |
│ └── test/ |
│ |
├── annotations/ # Annotations in PASCAL VOC .xml format |
│ ├── train/ |
│ ├── val/ |
│ └── test/ |
│ |
├── test_coco.json # Annotations for the test set in COCO .json format |
│ |
├── intersection.yaml # Dataset configuration file for YOLO |
├── classes.txt # List of class names |
│ |
├── convert_coco.py # Example script for coco format conversion (optional) |
│ |
└── README.md |
│ |
└── README_zh.md |
``` |
## **4. Annotation Formats** |
To maximize compatibility with various detection frameworks, we provide annotations in three standard formats: **YOLO**, **PASCAL VOC**, and **COCO (for the test set)**. |
### **4.1 YOLO Format (`.txt`)** |
Located in the `labels/` directory. Each image has a corresponding `.txt` file where each line represents an object. |
* **Format**: `<class_id> <x_center> <y_center> <width> <height>` (all values are normalized to `[0, 1]`). |
* **Example** (`image_001.txt` for a `1920x1080` image): |
```txt |
0 0.5416 0.6111 0.1041 0.1851 # A 'car' object |
2 0.2343 0.7870 0.1562 0.2222 # A 'truck' object |
``` |
### **4.2 PASCAL VOC Format (`.xml`)** |
Located in the `annotations/` directory. Each image has a corresponding `.xml` file containing object bounding boxes in absolute pixel coordinates. |
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