Poles_LeanedStraight/Classification/README.md

# Classification — Leaned vs Straight vs Rejected

- This dataset contains real-world images of electric utility poles labeled into three classes: **"Leaned"**, **"Straight"**, and **"Rejected"**. It is designed for **image-level classification tasks**.
- The dataset complements a previously released object detection dataset focused on bounding-box annotations.
- This module fine-tunes a DINOv2 ViT-B/14 model for **image-level classification** of poles.
- GitHub repo link: [Here](https://github.com/EPDCL/Electrical-Lines-Defect-Detection/tree/main/Pole_LeanedStraight_Defect/Classification)

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## Dataset Details


### Dataset Description

- **Task:** Image classification (multi-class)
- **Classes:** `Leaned`, `Straight`, `Rejected`
- **Image format:** `.jpg`
- **Label format:** Folder-based (one folder per class)

The images were collected by field linemen across three districts in Andhra Pradesh: **Visakhapatnam**, **Eluru**, and **Kakinada**. Each image was labeled by three annotators, with the final label selected via **majority vote**.

- **Curated by:** Sampath Balaji & team, at APEPDCL
- **Funded by:** Eastern Power Distribution Company of Andhra Pradesh Limited (APEPDCL)
- **License:** CC BY 4.0
- **Language(s):** Not applicable

## Uses
  
### Direct Use

This dataset is intended for training and evaluating machine learning models that classify pole alignment from real-world utility inspection photos.

### Out-of-Scope Use

- Surveillance, identity detection, or person-tracking
- Facial recognition or biometrics
- Applications requiring highly controlled camera perspectives

## Dataset Structure

The dataset is organized into pre-split folders:
```
Poles_LeanedStraight/Classification/
├── train/
│ ├── Leaned/
│ ├── Straight/
│ └── Rejected/
├── val/
│ ├── Leaned/
│ ├── Straight/
│ └── Rejected/
├── test/
│ ├── Leaned/
│ ├── Straight/
│ └── Rejected/
```
- `Rejected` indicates poles/images that were ambiguous or pole bottom or top was not visible or unusable for downstream tasks but still part of this classification problem.
- No augmentations were applied.
- Each split is approximately: **Train 75%**, **Val 15%**, **Test 10%**

## Dataset Creation

### Curation Rationale

This dataset was created to build an ML pipeline for real-time defect detection in utility poles, starting with identifying leaned poles as a foundational task. The aim is to build an electrical line quality monitoring system to revolutionize maintenance workflows.

### Source Data

#### Data Collection and Processing

Images were collected using mobile cameras by field linemen in Andhra Pradesh.

#### Image Capture Protocol

The images were captured on the ground by APEPDCL linemen following a field protocol designed to reduce bias and ensure consistency:

- Linemen were instructed to **hold the phone upright and parallel to the pole** to avoid tilt or skew that could distort the pole's perceived angle.
- Images were to include both the **top and bottom of the pole**, as full visibility is essential for lean classification.
- **No filters or enhancements** were applied during capture.

Despite this protocol, many images were captured with a **tilted horizon**, making it difficult to determine if a pole was leaned or not. Tools like **Google Photos** and **Adobe Lightroom** were used to **manually correct tilt** by straightening the horizon.

To prevent this in future data, the team identified an open-source camera app, **[Open Camera](https://opencamera.org.uk/)**, which uses the phone’s **gyroscope sensor to auto-straighten images**. This app is now recommended for field use to ensure more consistent alignment.

#### Observations and Future Protocol Improvements

During annotation, a key insight emerged:

- When images were taken **too close to the pole**, **perspective distortion** made it harder to determine pole alignment.
- Going forward, linemen will be instructed to **stand approximately one pole height away** to reduce distortion and improve labeling accuracy.

#### Who are the source data producers?

Electric linemen from APEPDCL working in Visakhapatnam, Eluru, and Kakinada districts of Andhra Pradesh, India.

### Annotations

#### Labeling Process
Images were individually labeled by 3 human labelers and saved in folder-based structure. **Majority vote** was used to determine the final class label. The team worked to maintain consistency, though **some labeling errors may persist due to the manual nature** of the task.

#### Who are the annotators?

The annotation team consisted of project members:  
- Srija Vanapalli  
- Krishna Sumwith  
- Jyotsna Adari  
Under the direction of the team lead.


#### Personal and Sensitive Information

No personal or sensitive data was intentionally included. However, some images may contain **people or faces captured incidentally** in public areas, as the data was collected in outdoor settings.

## Bias, Risks, and Limitations

- **Geographic bias:** All data comes from Andhra Pradesh, India — results may not generalize to other regions or environments.
- **Scene/perspective bias:** Variation in camera angles and distances may affect model training.
- **Privacy risk:** Images taken in public areas may contain identifiable people.

### Recommendations

- Apply **image normalization or augmentation** techniques to improve generalization.
- Account for **perspective and camera angle** effects during training and evaluation.
- Use ethically and responsibly, especially in systems that affect public infrastructure.

## Validation Accuracy

    - Best Val Acc: 84.14%
    - Hardware used: RTX 4070 Ti SUPER on [JOHNAIC](https://von-neumann.ai)

## Sample Visualizations
<p align="center"> <img src="https://raw.githubusercontent.com/sampath-balaji/Electrical-Lines-Defect-Detection/refs/heads/main/Pole_LeanedStraight_Defect/Classification/assets/val_viz_ep40.png" width="600"/> </p>

## More Information

Follow-up datasets in this series will include other electrical line and pole defects as part of APEPDCL's Line Quality monitoring project.

## Dataset Card Authors

- [Sampath Balaji](https://github.com/sampath-balaji)

## 📄 License
- [Code](https://github.com/sampath-balaji/EPDCL/tree/main/Pole_LeanedStraight_Defect/ObjectDetection): MIT License
- [Dataset](https://huggingface.co/datasets/EPDCL/Electrical-Lines-Defect-Detection/tree/main/Poles_LeanedStraight/Classification): CC BY 4.0