Power outages can disrupt daily domestic activities as well as the economy as operations are hampered when they occur. They can decrease work productivity by delaying operations that require electricity. The key solution to this problem is to ensure that there are fewer or no power interruptions. This can be achieved by ensuring secure and continuous network operations through regular maintenance and inspection. However, the traditional inspection technique of foot patrol is risky, laborious, and timeconsuming. A preferable contemporary technique uses an unmanned aerial vehicle (UAV) for inspecting distribution lines. Detecting power lines are crucial for real-time motion planning and navigation of UAVs. Previous techniques that depend on conventional filters and gradients may fail to detect power lines because of noisy backgrounds. Thus, this study proposes a novel technique by adopting the Transfer Learning approach The process involves fine-tuning a pretrained network from Point Instance Network (PINet) road lane detection model to fit the power line detection task. The proposed method extends the PINet model by adding a comparator for rotation block before it and a postprocessing block after it. This study generates four versions of the model, each of which was trained on one of the following datasets (i) self-gathered images captured by a handheld camera, (ii) a drone, (iii) publicly accessible images from the Power Line Dataset of Mountain Scene (PLDM), and (iv) Power Line Dataset of Urban Scene (PLDU). Experimental results on each dataset confirm the feasibility of the proposed approach.

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