An attention-guided adaptive multi-scale feature fusion method for remote sensing image object detection

Abstract

To address the issues of large scale variation, weak features of small objects, and complex background interference in optical remote sensing images, this paper proposes an attention-guided adaptive multi-scale fusion object detection method. In the feature extraction network, a Multi-scale Feature Extracted Module (MFEM) is constructed by introducing a Multi-Scale Channel Attention Mechanism (MS-CAM) to optimize the C3k2 structure, aiming to enhance multi-scale feature information and improve the feature representation capabilities of small objects. To suppress complex background interference, a joint Channel and Spatial Attention (CSA) mechanism is proposed. It focuses on target regions by filtering out redundant features in the channel dimension and strengthening the spatial representation of the targets. In the neck network, an Adaptive Fusion module (ADF) is built based on the CSA attention and integrated into the Extended Feature Pyramid Network (EFPN) architecture; this module enhances feature expression between adjacent layers and removes redundant information during the feature fusion process, thereby improving the network’s cross-scale feature integration capability. Experimental results demonstrate that on the DOTAv1.0 dataset, the proposed method achieves an increase of 3.5 percentage points in mean average precision (mAP) compared to the baseline model. Further evaluations on the HRSC2016 dataset show mAP improvements of 3.98 and 7.09 percentage points under the VOC07 and VOC12 metrics, respectively, verifying the robustness of the proposed model. The proposed method provides an effective solution for optical remote sensing object detection.