Power lines are one of the components in the electrical grid that needs persistent monitoring. Detecting the power lines can help UAV-based computer vision systems identify faults within the line, such as the damaged strand in the cable. Aside from fault detection, detecting the power lines also aids the UAV in its autonomous navigation and planning. However, with the protection of the shell to UAV, its presence in the images has become a challenge. In this paper, the authors investigate the feasibility of a deep neural network on an image dataset with occlusion. The paper enhances the Point Instance network to address the challenges of occlusion. The network is trained using synthetic images and tested on the image dataset obtained from the UAV prototype. The experimental results showed no significant difference in the network performance, even with or without the occlusion. The future work of this study involves further data acquisition of images using the prototype, especially at varying inspection sites.