| --- |
| license: cc-by-4.0 |
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| |
| # Object Detection — Leaned vs Straight Poles |
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| - This dataset contains real-world images of electric utility poles labeled as either **"Leaned_Pole"** or **"Straight_Pole"**, It represents the initial stage in a broader effort to identify various defects in electric poles using machine learning. |
| - This module trains and evaluates a YOLOv12 model to detect **Leaned_Pole** vs **Straight_Pole** in field images. |
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| ## Dataset Details |
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| The dataset is organized into pre-split folders: |
| ``` |
| Poles_LeanedStraight/ObjectDetection/ |
| ├── train/ |
| │ ├── images/ |
| │ └── labels/ |
| ├── valid/ |
| │ ├── images/ |
| │ └── labels/ |
| ├── test/ |
| │ ├── images/ |
| │ └── labels/ |
| └── data.yaml |
| ``` |
|
|
| ### Dataset Description |
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| The dataset comprises **1804 real-world annotated images** of electric poles collected across **Visakhapatnam, Eluru, and Kakinada** districts in Andhra Pradesh, India. These images were captured by utility linemen using mobile phone cameras during field inspections. |
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| Each image has been annotated in **YOLOv12 format** with one of two labels: |
| - `Leaned_Pole` |
| - `Straight_Pole` |
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|
| - **Curated by:** Sampath Balaji & team, at APEPDCL |
| - **Funded by [optional]:** Eastern Power Distribution Company of Andhra Pradesh Limited (APEPDCL) |
| - **Language(s) (NLP):** Not Applicable |
| - **License:** CC BY 4.0 |
|
|
| ### Dataset Sources |
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| - **GitHub Repository (code):** [Direct Link](https://github.com/EPDCL/Electrical-Lines-Defect-Detection/tree/main/Pole_LeanedStraight_Defect/ObjectDetection) |
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|
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| ## Uses |
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| ### Direct Use |
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| This dataset is intended to train, evaluate, and benchmark machine learning models for infrastructure monitoring, specifically for identifying leaned and straight poles in real-world, diverse conditions. |
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| ### Out-of-Scope Use |
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| - Surveillance or facial recognition tasks |
| - Biometric identification |
| - Any use that violates personal rights or local laws |
|
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| ## Dataset Structure |
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| - **Total images:** 1804 |
| - **Split:** |
| - Train: 1444 |
| - Validation: 181 |
| - Test: 179 |
| - **Labels:** `Leaned_Pole`, `Straight_Pole` |
| - **Format:** JPEG images with annotations in YOLOv12 format |
| - **Augmentations:** None applied |
|
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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. All annotations were manually reviewed and labeled in YOLOv12 format. |
|
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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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| #### Annotation process |
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| Manual bounding box annotations were created using image annotation tools and saved in YOLOv12 format. 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 APEPDCL project team, 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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| ## Metrics on dataset: |
| ### Validation Results (conf 0.35) |
| | Class | Precision | Recall | mAP\@0.5 | mAP\@0.5–0.95 | |
| | -------------- | --------- | --------- | --------- | ------------- | |
| | Leaned\_Pole | 0.894 | 0.894 | 0.963 | 0.730 | |
| | Straight\_Pole | 0.914 | 0.875 | 0.934 | 0.572 | |
| | **Overall** | **0.904** | **0.884** | **0.949** | **0.651** | |
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| ### Test Results (conf 0.35) |
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| | Class | Precision | Recall | mAP\@0.5 | mAP\@0.5–0.95 | |
| | -------------- | --------- | --------- | --------- | ------------- | |
| | Leaned\_Pole | 0.911 | 0.807 | 0.928 | 0.734 | |
| | Straight\_Pole | 0.922 | 0.879 | 0.968 | 0.630 | |
| | **Overall** | **0.917** | **0.843** | **0.948** | **0.682** | |
|
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| ## Sample predictions |
| <p align="center"> |
| <img src="https://raw.githubusercontent.com/sampath-balaji/Electrical-Lines-Defect-Detection/refs/heads/main/Pole_LeanedStraight_Defect/ObjectDetection/assets/output.jpeg" width="600"/> |
| </p> |
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| ## More Information [optional] |
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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/EPDCL/Electrical-Lines-Defect-Detection/tree/main/Pole_LeanedStraight_Defect/ObjectDetection): MIT License |
| - [Dataset](https://huggingface.co/datasets/EPDCL/Electrical-Lines-Defect-Detection/tree/main/Poles_LeanedStraight/ObjectDetection): CC BY 4.0 |
