A Real-World Large-Scale Infrared Image Dataset and Multitask Learning Framework for Power Line Surveillance

Nowadays, power line surveillance widely utilizes infrared images collected by drones for instance segmentation (IS) to determine whether the power equipment is abnormal, such as overheating. With the development of deep learning, state-of-the-art IS methods can be effectively applied to infrared images of electrical scenarios. However, the low spatial resolution and limited data scale of power line infrared images pose challenges for these methods. In this article, we make the following contributions: 1) we present a real-world large-scale dataset, CSG, which includes 11715 power line infrared images with a spatial resolution of $640 \times 512$ , and a total of 45145 instances. The CSG dataset covers 7 foreground objects coupled with different backgrounds, and has various scenarios from 295 geosites, providing a benchmark for evaluating the performance of IS methods and 2) we design a multitask learning (MTL) framework, PowerNet, where super-resolution (SR) and IS are unified into a collaborative network. SR allows the preservation of high-resolution (HR) feature representations, while IS incorporates them through a knowledge transfer pipeline (KTP). This enhances the discriminative ability of objects, particularly those that are small and complex, leading to more accurate predictions under the condition of low-resolution (LR) images. The pipeline measures task affinity for knowledge transfer and is effectively supervised by multitask loss. Extensive experiments reveal that the proposed framework achieves remarkable performance on the CSG dataset.