| # Classification — Leaned vs Straight vs Rejected |
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| - 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 |
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| ### Dataset Description |
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| - **Task:** Image classification (multi-class) |
| - **Classes:** `Leaned`, `Straight`, `Rejected` |
| - **Image format:** `.jpg` |
| - **Label format:** Folder-based (one folder per class) |
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| 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**. |
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| - **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 |
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| ## Uses |
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| ### Direct Use |
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| This dataset is intended for training and evaluating machine learning models that classify pole alignment from real-world utility inspection photos. |
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| ### Out-of-Scope Use |
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| - Surveillance, identity detection, or person-tracking |
| - Facial recognition or biometrics |
| - Applications requiring highly controlled camera perspectives |
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| ## Dataset Structure |
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| 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%** |
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| ## Dataset Creation |
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| ### Curation Rationale |
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| 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. |
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| ### Source Data |
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| #### Data Collection and Processing |
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| Images were collected using mobile cameras by field linemen in Andhra Pradesh. |
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| #### Image Capture Protocol |
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| The images were captured on the ground by APEPDCL linemen following a field protocol designed to reduce bias and ensure consistency: |
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| - 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. |
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| 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. |
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| 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. |
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| #### Observations and Future Protocol Improvements |
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| During annotation, a key insight emerged: |
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| - 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. |
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| #### Who are the source data producers? |
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| Electric linemen from APEPDCL working in Visakhapatnam, Eluru, and Kakinada districts of Andhra Pradesh, India. |
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| ### Annotations |
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| #### 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. |
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| #### Who are the annotators? |
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| The annotation team consisted of project members: |
| - Srija Vanapalli |
| - Krishna Sumwith |
| - Jyotsna Adari |
| Under the direction of the team lead. |
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| #### Personal and Sensitive Information |
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| 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. |
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| ## Bias, Risks, and Limitations |
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| - **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. |
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| ### Recommendations |
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| - 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. |
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| ## Validation Accuracy |
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| - Best Val Acc: 84.14% |
| - Hardware used: RTX 4070 Ti SUPER on [JOHNAIC](https://von-neumann.ai) |
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| ## 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> |
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| ## More Information |
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| Follow-up datasets in this series will include other electrical line and pole defects as part of APEPDCL's Line Quality monitoring project. |
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| ## Dataset Card Authors |
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| - [Sampath Balaji](https://github.com/sampath-balaji) |
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| ## 📄 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 |
