# A Multi-Scale Object Detection Network with Integrated Spatial-Channel Collaborative Attention for Remote Sensing Images

## TL;DR

This paper introduces a new network for detecting objects in remote sensing images that improves accuracy and efficiency using a novel attention mechanism.

## Contribution

The novel integration of a cross-channel multi-scale feature extraction module and a channel-spatial cross-attention mechanism for efficient and accurate multi-scale object detection.

## Key findings

- The model achieves 78.1% mAP on DIOR, 90.6% on HRRSD, and 96.5% on RSOD datasets.
- It outperforms YOLOv11 and YOLOv8 in accuracy while maintaining lower computational complexity.
- The proposed method balances detection accuracy and efficiency with 19.5 M parameters and 75.2 G FLOPs.

## Abstract

What are the main findings?

The proposed model, which integrates a novel cross-channel multi-scale feature extraction (CC-MSFE) module and a channel-spatial cross-attention (CSCA) mechanism, achieves good performance on three public remote sensing datasets (DIOR: 78.1% mAP, HRRSD: 90.6% mAP, and RSOD: 96.5% mAP).

The framework effectively performs multi-scale object detection within complex environments, achieving enhanced accuracy while reducing computational complexity (Parameters: 19.5 M, FLOPs: 75.2 G).

What are the implications of the main findings?

It provides a practical and efficient solution for multi-scale object detection in complex remote sensing scenarios.

This innovative cross-attention structure design provides practical guidance for other feature extraction scenarios in remote sensing applications.

In remote sensing object detection, current models typically employ feature extraction modules and attention mechanisms to tackle issues such as significant scale variations among targets, cluttered backgrounds, and the subtle characteristics of small objects. Nevertheless, existing feature extraction approaches often depend on convolution kernels with fixed sizes, which can blur the contours of large objects and provide inadequate feature representation for small objects. Moreover, many attention mechanisms simply combine spatial and channel attention, without fully considering the deep integration between spatial and channel features, consequently leading to high-dimensional features and considerable computational overhead. To overcome these shortcomings, this paper introduces a multi-scale object detection network with integrated spatial-channel collaborative attention for remote sensing images. This approach enhances feature perception and representation for multi-scale targets, particularly small targets, through the design of the cross-channel multi-scale feature extraction module (CC-MSFE). Furthermore, a new channel-spatial cross-attention mechanism (CSCA) is introduced, comprising the channel attention mechanism (CA), the spatial attention mechanism (SA), and the cross-attention fusion module (CAFM). This design fosters dynamic interaction and joint optimization across channel and spatial dimensions, thereby improving detection accuracy while effectively reducing computational cost. The efficacy of the proposed model is evaluated on three publicly available remote sensing datasets. Experimental results show that the model achieves a mAP of 78.1% on the DIOR dataset and of 90.6% on the HRRSD dataset, outperforming YOLOv11 by 0.7% and 1.4%, respectively. On the RSOD dataset, it attains a mAP of 96.5%, surpassing YOLOv8 by 2.1%. In addition, the proposed method maintains a notably lower parameter count and computational complexity compared to existing approaches, achieving an effective balance between detection accuracy and computational efficiency.