# InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images

arXiv: https://arxiv.org/abs/2311.01619 · HTML: https://mlsanity.com/paper/2311.01619/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 68 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://mlsanity.com/paper/2006.02334/) (2020)
- [TOOD: Task-aligned One-stage Object Detection](https://mlsanity.com/paper/2108.07755/) (2021)
- [Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows](https://mlsanity.com/paper/2008.12577/) (2020)
- [CenterMask : Real-Time Anchor-Free Instance Segmentation](https://mlsanity.com/paper/1911.06667/) (2019)
- [Deep Industrial Image Anomaly Detection: A Survey](https://mlsanity.com/paper/2301.11514/) (2023)
- [Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection](https://mlsanity.com/paper/2110.02855/) (2021)
- [Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm](https://mlsanity.com/paper/2004.07657/) (2020)
- [PlantDoc: A Dataset for Visual Plant Disease Detection](https://mlsanity.com/paper/1911.10317/) (2019)
- [TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines](https://mlsanity.com/paper/2010.10032/) (2020)
- [Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction](https://mlsanity.com/paper/2304.02216/) (2023)
- [Sports Video Analysis on Large-Scale Data](https://mlsanity.com/paper/2208.04897/) (2022)

## Built on by (most cited first, 15 of 15)

- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset](https://mlsanity.com/paper/2409.20353/) (2024)
- [Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study](https://mlsanity.com/paper/2311.02747/) (2023)
- [An LLM-based Framework for Human-Swarm Teaming Cognition in Disaster Search and Rescue](https://mlsanity.com/paper/2511.04042/) (2025)
- [Unleashing Foundation Vision Models: Adaptive Transfer for Diverse Data-Limited Scientific Domains](https://mlsanity.com/paper/2512.22664/) (2025)
- [Power Line Aerial Image Restoration under dverse Weather: Datasets and Baselines](https://mlsanity.com/paper/2409.04812/) (2024)
- [An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures](https://mlsanity.com/paper/2509.05490/) (2025)
- [Comparing Object Detection Models for Electrical Substation Component Mapping](https://mlsanity.com/paper/2512.22454/) (2025)
- [GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure](https://mlsanity.com/paper/2601.13052/) (2026)
- [Synthetic Defect Image Generation for Power Line Insulator Inspection Using Multimodal Large Language Models](https://mlsanity.com/paper/2603.08069/) (2026)
- [PoiCGAN: A Targeted Poisoning Based on Feature-Label Joint Perturbation in Federated Learning](https://mlsanity.com/paper/2603.23574/) (2026)
- [EDFNet: Early Fusion of Edge and Depth for Thin-Obstacle Segmentation in UAV Navigation](https://mlsanity.com/paper/2604.09694/) (2026)
- [DenseScout: Algorithm-System Co-design for Budgeted Tiny Object Selection on Edge Platforms](https://mlsanity.com/paper/2604.25300/) (2026)
- [Dense Coordinate-List Fine-Tuning Induces a Controllable Interference Surface in Vision-Language Models](https://mlsanity.com/paper/2606.14507/) (2026)
- [Beyond Balanced Accuracy: A Resolution and Parity-Controlled Benchmark for Vision-Language and Vision-Only Defect Assessment in UAV Power-Line Inspection](https://mlsanity.com/paper/2609.27457/) (2026)

## Abstract (arXiv)

Power line maintenance and inspection are essential to avoid power supply interruptions, reducing its high social and financial impacts yearly. Automating power line visual inspections remains a relevant open problem for the industry due to the lack of public real-world datasets of power line components and their various defects to foster new research. This paper introduces InsPLAD, a Power Line Asset Inspection Dataset and Benchmark containing 10,607 high-resolution Unmanned Aerial Vehicles colour images. The dataset contains seventeen unique power line assets captured from real-world operating power lines. Additionally, five of those assets present six defects: four of which are corrosion, one is a broken component, and one is a bird's nest presence. All assets were labelled according to their condition, whether normal or the defect name found on an image level. We thoroughly evaluate state-of-the-art and popular methods for three image-level computer vision tasks covered by InsPLAD: object detection, through the AP metric; defect classification, through Balanced Accuracy; and anomaly detection, through the AUROC metric. InsPLAD offers various vision challenges from uncontrolled environments, such as multi-scale objects, multi-size class instances, multiple objects per image, intra-class variation, cluttered background, distinct point-of-views, perspective distortion, occlusion, and varied lighting conditions. To the best of our knowledge, InsPLAD is the first large real-world dataset and benchmark for power line asset inspection with multiple components and defects for various computer vision tasks, with a potential impact to improve state-of-the-art methods in the field. It will be publicly available in its integrity on a repository with a thorough description. It can be found at https://github.com/andreluizbvs/InsPLAD.