# 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) --- ## 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

## 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