# Prompt Learning for Oriented Power Transmission Tower Detection in High-Resolution SAR Images

arXiv: https://arxiv.org/abs/2404.01074 · HTML: https://mlsanity.com/paper/2404.01074/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 9 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model](https://mlsanity.com/paper/2203.14940/) (2022)
- [Prompt Distribution Learning](https://mlsanity.com/paper/2205.03340/) (2022)
- [R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object](https://mlsanity.com/paper/1908.05612/) (2019)
- [Align Deep Features for Oriented Object Detection](https://mlsanity.com/paper/2008.09397/) (2020)
- [Fine-grained Visual-Text Prompt-Driven Self-Training for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2211.00849/) (2022)
- [UAVs as Mobile Infrastructure: Addressing Battery Lifetime](https://mlsanity.com/paper/1807.00996/) (2018)

## Abstract (arXiv)

Detecting transmission towers from synthetic aperture radar (SAR) images remains a challenging task due to the comparatively small size and side-looking geometry, with background clutter interference frequently hindering tower identification. A large number of interfering signals superimposes the return signal from the tower. We found that localizing or prompting positions of power transmission towers is beneficial to address this obstacle. Based on this revelation, this paper introduces prompt learning into the oriented object detector (P2Det) for multimodal information learning. P2Det contains the sparse prompt coding and cross-attention between the multimodal data. Specifically, the sparse prompt encoder (SPE) is proposed to represent point locations, converting prompts into sparse embeddings. The image embeddings are generated through the Transformer layers. Then a two-way fusion module (TWFM) is proposed to calculate the cross-attention of the two different embeddings. The interaction of image-level and prompt-level features is utilized to address the clutter interference. A shape-adaptive refinement module (SARM) is proposed to reduce the effect of aspect ratio. Extensive experiments demonstrated the effectiveness of the proposed model on high-resolution SAR images. P2Det provides a novel insight for multimodal object detection due to its competitive performance.