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A context-aware transformer with weighted residual for efficient remote sensing target detection.

Zhongyu Li^1,2,3,4, Yimin Shen¹, Xiaoping Jing¹

¹Chengdu Technological University, Chengdu, China.

Frontiers in Neurorobotics

|June 1, 2026

Summary

This study introduces an improved remote sensing image target detection model for disaster management. The novel approach enhances multi-scale feature fusion and contextual information representation, achieving superior detection accuracy.

Keywords:

disaster remote sensing images loss imbalance rotated target target detection weighted residual pyramid

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Area of Science:

Computer Vision
Remote Sensing
Artificial Intelligence

Background:

Remote sensing image target detection is crucial for disaster prevention and mitigation.
Existing models struggle with multi-scale feature fusion, contextual information, and class imbalance.

Purpose of the Study:

To develop an advanced target detection model for remote sensing images.
To address limitations in feature fusion, context representation, and training imbalance.

Main Methods:

A novel model combining a context transformer and a weighted residual pyramid.
Introduction of a learnable balancing factor to mitigate layer contribution imbalance.
Utilizing rotated bounding boxes and a CIoU-based multi-task loss function.

Main Results:

The proposed model achieves mAP@0.5 scores of 0.754 (DOTAv1.0) and 0.714 (DOTAv1.5).
Demonstrates consistent improvements and ensures real-time performance.
Effective visualization across various disaster scenarios highlights practical value.

Conclusions:

The developed model significantly enhances remote sensing target detection capabilities.
It offers a robust solution for disaster prevention and mitigation applications.
The model shows strong practical value and real-time performance.