EMWMS-YOLO: Efficient Multi-Scale Detection Framework for Small Objects in Challenging Remote Sensing Scenes.
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| Title: | EMWMS-YOLO: Efficient Multi-Scale Detection Framework for Small Objects in Challenging Remote Sensing Scenes. |
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| Authors: | Tian, Shuo1 (AUTHOR), Li, Yuguo1 (AUTHOR), Li, Jian1 (AUTHOR), Sun, Wenzheng1 (AUTHOR), Chen, Longfa1 (AUTHOR), Meng, Na1 (AUTHOR) skd993317@sdust.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1682. 27p. |
| Subjects: | Remote sensing, Object recognition (Computer vision), Computer vision, Deep learning |
| Abstract: | Highlights: What are the main findings? To address the challenges of multi-scale target detection, we propose an Efficient Multi-Scale Cross-Layer Extraction (EMSCLE) backbone framework. By effectively capturing and leveraging multi-scale features, the network enables comprehensive fusion of hierarchical information, thereby enhancing its representation capability in complex multi-scale detection scenarios. To address the challenges posed by complex background interference, a Multi-Scale Adaptive Feature Fusion (MSAFF) neck architecture is introduced. By performing efficient multi-scale feature fusion and incorporating an adaptive channel enhancement mechanism, the model selectively strengthens responses to informative features while suppressing redundant ones, thereby improving its capability to detect small objects in complex background scenarios. We design a set of core modules, including Dual-Branch Feature Extraction (DBFE), Multi-Scale Feature Perception (MSFP), Spatial Pyramid Pooling Fast with Large Separable Kernel Attention (SPPF-LSKA), Channel-Enhanced Convolution (CEC), Multi-Scale Gated Feature Fusion (MSGFF), and WaveletPool, along with the Detect-MultiSEAM detection head. These components collectively enhance the model's ability to perceive, represent, and interact with multi-scale features, thereby enabling more effective detection of small-scale targets in complex scenes. We introduce the ShapeIoU loss function, which effectively addresses the insensitivity of standard IoU to the localization of elongated, irregular, or small targets, thereby enabling more accurate shape fitting in detection results. What are the implications of the main findings? The DBFE module employs dual convolutional branches to capture multi-scale features while enhancing cross-channel information interaction and fine-grained feature representation. The MSFP module performs multi-scale feature fusion by leveraging the complementary strengths of MANet and FasterNet-Block, thereby improving the model's capability in multi-scale detection tasks. The SPPF-LSKA module expands the receptive field by integrating multi-scale features and introducing a large-kernel separable attention mechanism, which enhances feature representation while reducing computational redundancy. The CEC module combines MBConv and EffectiveSE to adaptively recalibrate channel-wise responses, thereby strengthening the representation of small and low-contrast targets while alleviating performance degradation caused by background interference. The MSGFF module integrates EfficientViM and CGLU to emphasize salient features and suppress redundant information, improving the network's effectiveness in detecting small objects under complex backgrounds. The WaveletPool module utilizes wavelet decomposition to achieve information-preserving downsampling, enhancing the representation of structural and fine-detail features while reducing spatial resolution, thus benefiting small-object detection. Finally, the Detect-MultiSEAM detection head optimizes feature representation through a multi-scale spatial enhancement mechanism, improving detection accuracy while reducing missed detections and false positives. The proposed EMWMS-YOLO is evaluated on two benchmark datasets, VEDAI and NWPU-VHR-10. Experimental results demonstrate that it outperforms the YOLOv11n baseline by 9.8% and 4.1% in terms of mAP50, respectively, highlighting its effectiveness in small-object detection. Nowadays, remote sensing images are characterized by significant scale variations, a high density of small targets, and complex background conditions, which pose substantial challenges for small-object detection. To address these issues, we propose EMWMS-YOLO, a lightweight and efficient detection framework built upon YOLOv11n. Specifically, an Efficient Multi-Scale Cross-Layer Extraction (EMSCLE) backbone is designed by integrating the Dual-Branch Feature Extraction (DBFE), Multi-Scale Feature Perception (MSFP), and Spatial Pyramid Pooling Fast with Large Separable Kernel Attention (SPPF-LSKA) modules, enabling effective multi-scale feature extraction and cross-channel interaction. Furthermore, a Multi-Scale Adaptive Feature Fusion (MSAFF) neck architecture, composed of the Channel-Enhanced Convolution (CEC) and Multi-Scale Gated Feature Fusion (MSGFF) modules, is introduced to dynamically fuse cross-scale features and enhance salient target responses while suppressing background noise. In addition, the WaveletPool module replaces conventional pooling operations to reduce information loss and feature aliasing while preserving structural details. A Detect-MultiSEAM detection head is constructed by embedding a multi-scale spatial enhancement attention mechanism, which improves feature representation under complex conditions and reduces missed detections and false positives. Finally, the ShapeIoU loss function is employed to better model geometric and morphological properties, thereby improving localization accuracy. Experimental results on the VEDAI and NWPU-VHR-10 datasets demonstrate that the proposed method achieves improvements of 9.8% and 4.1% in mAP50 over the YOLOv11n baseline, respectively, verifying its effectiveness in small-object detection. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? To address the challenges of multi-scale target detection, we propose an Efficient Multi-Scale Cross-Layer Extraction (EMSCLE) backbone framework. By effectively capturing and leveraging multi-scale features, the network enables comprehensive fusion of hierarchical information, thereby enhancing its representation capability in complex multi-scale detection scenarios. To address the challenges posed by complex background interference, a Multi-Scale Adaptive Feature Fusion (MSAFF) neck architecture is introduced. By performing efficient multi-scale feature fusion and incorporating an adaptive channel enhancement mechanism, the model selectively strengthens responses to informative features while suppressing redundant ones, thereby improving its capability to detect small objects in complex background scenarios. We design a set of core modules, including Dual-Branch Feature Extraction (DBFE), Multi-Scale Feature Perception (MSFP), Spatial Pyramid Pooling Fast with Large Separable Kernel Attention (SPPF-LSKA), Channel-Enhanced Convolution (CEC), Multi-Scale Gated Feature Fusion (MSGFF), and WaveletPool, along with the Detect-MultiSEAM detection head. These components collectively enhance the model's ability to perceive, represent, and interact with multi-scale features, thereby enabling more effective detection of small-scale targets in complex scenes. We introduce the ShapeIoU loss function, which effectively addresses the insensitivity of standard IoU to the localization of elongated, irregular, or small targets, thereby enabling more accurate shape fitting in detection results. What are the implications of the main findings? The DBFE module employs dual convolutional branches to capture multi-scale features while enhancing cross-channel information interaction and fine-grained feature representation. The MSFP module performs multi-scale feature fusion by leveraging the complementary strengths of MANet and FasterNet-Block, thereby improving the model's capability in multi-scale detection tasks. The SPPF-LSKA module expands the receptive field by integrating multi-scale features and introducing a large-kernel separable attention mechanism, which enhances feature representation while reducing computational redundancy. The CEC module combines MBConv and EffectiveSE to adaptively recalibrate channel-wise responses, thereby strengthening the representation of small and low-contrast targets while alleviating performance degradation caused by background interference. The MSGFF module integrates EfficientViM and CGLU to emphasize salient features and suppress redundant information, improving the network's effectiveness in detecting small objects under complex backgrounds. The WaveletPool module utilizes wavelet decomposition to achieve information-preserving downsampling, enhancing the representation of structural and fine-detail features while reducing spatial resolution, thus benefiting small-object detection. Finally, the Detect-MultiSEAM detection head optimizes feature representation through a multi-scale spatial enhancement mechanism, improving detection accuracy while reducing missed detections and false positives. The proposed EMWMS-YOLO is evaluated on two benchmark datasets, VEDAI and NWPU-VHR-10. Experimental results demonstrate that it outperforms the YOLOv11n baseline by 9.8% and 4.1% in terms of mAP50, respectively, highlighting its effectiveness in small-object detection. Nowadays, remote sensing images are characterized by significant scale variations, a high density of small targets, and complex background conditions, which pose substantial challenges for small-object detection. To address these issues, we propose EMWMS-YOLO, a lightweight and efficient detection framework built upon YOLOv11n. Specifically, an Efficient Multi-Scale Cross-Layer Extraction (EMSCLE) backbone is designed by integrating the Dual-Branch Feature Extraction (DBFE), Multi-Scale Feature Perception (MSFP), and Spatial Pyramid Pooling Fast with Large Separable Kernel Attention (SPPF-LSKA) modules, enabling effective multi-scale feature extraction and cross-channel interaction. Furthermore, a Multi-Scale Adaptive Feature Fusion (MSAFF) neck architecture, composed of the Channel-Enhanced Convolution (CEC) and Multi-Scale Gated Feature Fusion (MSGFF) modules, is introduced to dynamically fuse cross-scale features and enhance salient target responses while suppressing background noise. In addition, the WaveletPool module replaces conventional pooling operations to reduce information loss and feature aliasing while preserving structural details. A Detect-MultiSEAM detection head is constructed by embedding a multi-scale spatial enhancement attention mechanism, which improves feature representation under complex conditions and reduces missed detections and false positives. Finally, the ShapeIoU loss function is employed to better model geometric and morphological properties, thereby improving localization accuracy. Experimental results on the VEDAI and NWPU-VHR-10 datasets demonstrate that the proposed method achieves improvements of 9.8% and 4.1% in mAP50 over the YOLOv11n baseline, respectively, verifying its effectiveness in small-object detection. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111682 |
