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

This article has been accepted for publication in the International Journal of Remote Sensing, published by Taylor & Francis.

InsPLAD is a Power Line Asset Inspection Dataset and Benchmark containing 10,607 high-resolution Unmanned Aerial Vehicles color images. The dataset contains seventeen unique power line assets captured in the real world and annotated for object detection. In addition, five assets presented six types of defects, annotated into normal and defective samples on an image level. We thoroughly evaluate state-of-the-art and popular methods for three computer vision tasks covered by InsPLAD: object detection, image classification, and anomaly detection. The first aims to detect power line assets in UAV images, and the other two to classify defects in cropped power line asset images. InsPLAD offers a wide range of vision-related challenges, such as multi-scale objects, multi-size class instances, multiple objects per image, intra-class variation, cluttered background, uncontrolled environment, distinct point-of-views, perspective distortion, occlusion, and varied lighting conditions. Our benchmark indicates considerable room for improvement in the state-of-the-art methods. InsPLAD is the first large real-world dataset for power line asset inspection with multiple components and defects.