Vasileios N Kouris, Eleni Vrochidou, George A Papakostas
Electricity is fundamental for all modern infrastructures, while disruptions in transmission networks could result in severe failures in healthcare, transportation, communication, industry, and all related to public safety. However, inspection of overhead lines and their components is costly and labor-intensive. The convergence of Unmanned Aerial Vehicles (UAVs), advanced imaging sensors, and deep-learning-based Computer Vision has reshaped this domain. To this end, this work presents a systematic review of Computer Vision applications in electric power transmission line inspection. From an initial 1493 Scopus records, 148 studies published between 2018 and 2026 were retained through a transparent, multi-stage screening and quality-scoring process based on PRISMA guidelines. The reviewed literature was synthesized across four axes: (1) monitoring platforms and sensor technologies, (2) Computer Vision approaches per vision task, (3) datasets, metrics, and evaluation practices, and (4) synthesis of results and industrial adoption. The analysis of the literature confirms the dominance of You Only Look Once (YOLO) family models for real-time edge deployment and the rising adoption of Transformer architectures, while exposing persistent gaps in dataset availability, domain generalization, and field validation. The review concludes with the open challenges and concrete future directions toward fully autonomous, robust, and sustainable inspection systems.