--- license: cc-by-4.0 --- # Object Detection — Leaned vs Straight Poles - 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. ## Dataset Details The dataset is organized into pre-split folders: ``` Poles_LeanedStraight/ObjectDetection/ ├── train/ │ ├── images/ │ └── labels/ ├── valid/ │ ├── images/ │ └── labels/ ├── test/ │ ├── images/ │ └── labels/ └── data.yaml ``` ### Dataset Description 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. Each image has been annotated in **YOLOv12 format** with one of two labels: - `Leaned_Pole` - `Straight_Pole` - **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 - **GitHub Repository (code):** [Direct Link](https://github.com/EPDCL/Electrical-Lines-Defect-Detection/tree/main/Pole_LeanedStraight_Defect/ObjectDetection) ## Uses ### Direct Use 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. ### Out-of-Scope Use - Surveillance or facial recognition tasks - Biometric identification - Any use that violates personal rights or local laws ## Dataset Structure - **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 ## 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. All annotations were manually reviewed and labeled in YOLOv12 format. #### 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 #### Annotation process 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. #### Who are the annotators? The APEPDCL project team, 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. ## 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** | ### Test Results (conf 0.35) | 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** | ## Sample predictions

## More Information [optional] 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/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