A review and future directions of techniques for extracting powerlines and pylons from LiDAR point clouds

The rapid progression of the intelligent grid requires continuous vigilance in monitoring and maintaining extensive powerline corridors to ensure their safety. In this context, LiDAR technology, renowned for its exceptional precision and reduced vulnerability to external interference, emerges as a valuable alternative for monitoring powerline corridors. This contrasts with conventional methods such as manual field inspections and imprecise sensors. However, the vast amount of data generated by LiDAR presents significant challenges, including scene noise, diverse scenarios, and unwanted objects proximate to powerlines or pylons. These factors complicate the accurate extraction and analysis of relevant data from point clouds produced by LiDAR. This review examines recent methodologies aimed at overcoming these challenges. It begins with a brief exploration of data collection systems for powerline corridors, including TLS, MLS, UAVLS, ALS, and CIR, highlighting their respective merits and drawbacks. The subsequent sections of the review provide a comprehensive overview of three methodological categories: tracking and detection-based approaches, machine learning-based techniques, and deep learning-based methods. Within each category, representative techniques are delineated, elucidating their potential, limitations, and applicable domains. This review incorporates qualitative analysis to enhance researchers' comprehension of current studies and to providea nuanced understanding of the strengths and weaknesses of these techniques. In a departure from previous research, this review extends its focus beyond powerline extraction to include the extraction of pylons and single wires. It identifies a notable oversight in the lack of emphasis on individual wire extraction, attributing this to challenges posed by wire proximity, and highlights limited attention to pylon extraction near vegetation. While machine learning and deep learning methods offer heightened automation, persistent issues such as the requirement for extensive labeled samples and inadequate model generalization, underscore the need for continued efforts to address these challenges. This discussion emphasizes the necessity of overcoming these hurdles to boost ongoing advancements in powerline and pylon extraction techniques.