Adapting Segment Anything Model for Power Transmission Corridor Hazard Segmentation

TL;DR

This paper adapts the Segment Anything Model for power transmission corridor hazard segmentation by introducing a context-aware prompt adapter and a high-fidelity mask decoder, along with creating a large-scale dataset, ELE-40K.

Contribution

It proposes ELE-SAM, a novel adaptation of SAM with specialized modules for complex hazard segmentation and introduces the ELE-40K benchmark dataset.

Findings

ELE-SAM outperforms baseline with 16.8% mIoU improvement

Achieves 20.6% mBIoU gain over baseline

Demonstrates superior performance on high-quality object segmentation

Abstract

Power transmission corridor hazard segmentation (PTCHS) aims to separate transmission equipment and surrounding hazards from complex background, conveying great significance to maintaining electric power transmission safety. Recently, the Segment Anything Model (SAM) has emerged as a foundational vision model and pushed the boundaries of segmentation tasks. However, SAM struggles to deal with the target objects in complex transmission corridor scenario, especially those with fine structure. In this paper, we propose ELE-SAM, adapting SAM for the PTCHS task. Technically, we develop a Context-Aware Prompt Adapter to achieve better prompt tokens via incorporating global-local features and focusing more on key regions. Subsequently, to tackle the hazard objects with fine structure in complex background, we design a High-Fidelity Mask Decoder by leveraging multi-granularity mask features and then scaling them to a higher resolution. Moreover, to train ELE-SAM and advance this field, we construct the ELE-40K benchmark, the first large-scale and real-world dataset for PTCHS including 44,094 image-mask pairs. Experimental results for ELE-40K demonstrate the superior performance that ELE-SAM outperforms the baseline model with the average 16.8% mIoU and 20.6% mBIoU performance improvement. Moreover, compared with the state-of-the-art method on HQSeg-44K, the average 2.9% mIoU and 3.8% mBIoU absolute improvements further validate the effectiveness of our method on high-quality generic object segmentation. The source code and dataset are available at https://github.com/Hhaizee/ELE-SAM.