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.

翻译：电力线路的维护与检测对于避免供电中断、降低每年由此产生的高昂社会与经济影响至关重要。由于缺乏公开的、包含电力线路组件及其各类缺陷的真实世界数据集以推动新研究，自动化电力线路视觉检测在工业领域仍是一个具有挑战性的开放问题。本文介绍了InsPLAD——一个电力线路资产检测数据集与基准，包含10,607张高分辨率无人机彩色图像。该数据集涵盖了从实际运行电力线路中采集的十七种独特的电力线路资产。此外，其中五种资产存在六类缺陷：四种腐蚀、一种部件断裂以及一种鸟巢附着。所有资产均根据其状态（正常或图像中标注的缺陷名称）进行了标记。我们通过AP指标（目标检测）、平衡准确率（缺陷分类）及AUROC指标（异常检测）这三个图像级计算机视觉任务，对InsPLAD上最先进及流行的多种方法进行了全面评估。InsPLAD呈现了非受控环境中的多种视觉挑战，例如多尺度目标、不同尺寸的类别实例、单图多目标、类内差异、杂乱背景、不同视角、透视畸变、遮挡以及多变光照条件。据我们所知，InsPLAD是首个面向多种计算机视觉任务、包含多个组件与缺陷的电力线路资产检测大规模真实世界数据集与基准，有望推动该领域最先进方法的改进。该数据集将完整地以开源方式发布于一个包含详尽描述的仓库中，访问地址为：https://github.com/andreluizbvs/InsPLAD。