A Novel Dynamic Context Branch Attention Network for Detecting Small Objects in Remote Sensing Images
Detecting small objects in remote sensing images is challenging due to their size, which results in limited distinctive features. This limitation necessitates the effective use of contextual information for accurate identification. Many existing methods often struggle because they do not dynamically...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/14/2415 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850071492475224064 |
|---|---|
| author | Huazhong Jin Yizhuo Song Ting Bai Kaimin Sun Yepei Chen |
| author_facet | Huazhong Jin Yizhuo Song Ting Bai Kaimin Sun Yepei Chen |
| author_sort | Huazhong Jin |
| collection | DOAJ |
| description | Detecting small objects in remote sensing images is challenging due to their size, which results in limited distinctive features. This limitation necessitates the effective use of contextual information for accurate identification. Many existing methods often struggle because they do not dynamically adjust the contextual scope based on the specific characteristics of each target. To address this issue and improve the detection performance of small objects (typically defined as objects with a bounding box area of less than 1024 pixels), we propose a novel backbone network called the Dynamic Context Branch Attention Network (DCBANet). We present the Dynamic Context Scale-Aware (DCSA) Block, which utilizes a multi-branch architecture to generate features with diverse receptive fields. Within each branch, a Context Adaptive Selection Module (CASM) dynamically weights information, allowing the model to focus on the most relevant context. To further enhance performance, we introduce an Efficient Branch Attention (EBA) module that adaptively reweights the parallel branches, prioritizing the most discriminative ones. Finally, to ensure computational efficiency, we design a Dual-Gated Feedforward Network (DGFFN), a lightweight yet powerful replacement for standard FFNs. Extensive experiments conducted on four public remote sensing datasets demonstrate that the DCBANet achieves impressive mAP@0.5 scores of 80.79% on DOTA, 89.17% on NWPU VHR-10, 80.27% on SIMD, and a remarkable 42.4% mAP@0.5:0.95 on the specialized small object benchmark AI-TOD. These results surpass RetinaNet, YOLOF, FCOS, Faster R-CNN, Dynamic R-CNN, SKNet, and Cascade R-CNN, highlighting its effectiveness in detecting small objects in remote sensing images. However, there remains potential for further improvement in multi-scale and weak target detection. Future work will integrate local and global context to enhance multi-scale object detection performance. |
| format | Article |
| id | doaj-art-a753f80c4aa1477ab90bcb1be623bf98 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-a753f80c4aa1477ab90bcb1be623bf982025-08-20T02:47:17ZengMDPI AGRemote Sensing2072-42922025-07-011714241510.3390/rs17142415A Novel Dynamic Context Branch Attention Network for Detecting Small Objects in Remote Sensing ImagesHuazhong Jin0Yizhuo Song1Ting Bai2Kaimin Sun3Yepei Chen4School of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430010, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaDetecting small objects in remote sensing images is challenging due to their size, which results in limited distinctive features. This limitation necessitates the effective use of contextual information for accurate identification. Many existing methods often struggle because they do not dynamically adjust the contextual scope based on the specific characteristics of each target. To address this issue and improve the detection performance of small objects (typically defined as objects with a bounding box area of less than 1024 pixels), we propose a novel backbone network called the Dynamic Context Branch Attention Network (DCBANet). We present the Dynamic Context Scale-Aware (DCSA) Block, which utilizes a multi-branch architecture to generate features with diverse receptive fields. Within each branch, a Context Adaptive Selection Module (CASM) dynamically weights information, allowing the model to focus on the most relevant context. To further enhance performance, we introduce an Efficient Branch Attention (EBA) module that adaptively reweights the parallel branches, prioritizing the most discriminative ones. Finally, to ensure computational efficiency, we design a Dual-Gated Feedforward Network (DGFFN), a lightweight yet powerful replacement for standard FFNs. Extensive experiments conducted on four public remote sensing datasets demonstrate that the DCBANet achieves impressive mAP@0.5 scores of 80.79% on DOTA, 89.17% on NWPU VHR-10, 80.27% on SIMD, and a remarkable 42.4% mAP@0.5:0.95 on the specialized small object benchmark AI-TOD. These results surpass RetinaNet, YOLOF, FCOS, Faster R-CNN, Dynamic R-CNN, SKNet, and Cascade R-CNN, highlighting its effectiveness in detecting small objects in remote sensing images. However, there remains potential for further improvement in multi-scale and weak target detection. Future work will integrate local and global context to enhance multi-scale object detection performance.https://www.mdpi.com/2072-4292/17/14/2415small object detectiondynamic context informationbranch attention networklightweight backbone networkremote sensing images |
| spellingShingle | Huazhong Jin Yizhuo Song Ting Bai Kaimin Sun Yepei Chen A Novel Dynamic Context Branch Attention Network for Detecting Small Objects in Remote Sensing Images Remote Sensing small object detection dynamic context information branch attention network lightweight backbone network remote sensing images |
| title | A Novel Dynamic Context Branch Attention Network for Detecting Small Objects in Remote Sensing Images |
| title_full | A Novel Dynamic Context Branch Attention Network for Detecting Small Objects in Remote Sensing Images |
| title_fullStr | A Novel Dynamic Context Branch Attention Network for Detecting Small Objects in Remote Sensing Images |
| title_full_unstemmed | A Novel Dynamic Context Branch Attention Network for Detecting Small Objects in Remote Sensing Images |
| title_short | A Novel Dynamic Context Branch Attention Network for Detecting Small Objects in Remote Sensing Images |
| title_sort | novel dynamic context branch attention network for detecting small objects in remote sensing images |
| topic | small object detection dynamic context information branch attention network lightweight backbone network remote sensing images |
| url | https://www.mdpi.com/2072-4292/17/14/2415 |
| work_keys_str_mv | AT huazhongjin anoveldynamiccontextbranchattentionnetworkfordetectingsmallobjectsinremotesensingimages AT yizhuosong anoveldynamiccontextbranchattentionnetworkfordetectingsmallobjectsinremotesensingimages AT tingbai anoveldynamiccontextbranchattentionnetworkfordetectingsmallobjectsinremotesensingimages AT kaiminsun anoveldynamiccontextbranchattentionnetworkfordetectingsmallobjectsinremotesensingimages AT yepeichen anoveldynamiccontextbranchattentionnetworkfordetectingsmallobjectsinremotesensingimages AT huazhongjin noveldynamiccontextbranchattentionnetworkfordetectingsmallobjectsinremotesensingimages AT yizhuosong noveldynamiccontextbranchattentionnetworkfordetectingsmallobjectsinremotesensingimages AT tingbai noveldynamiccontextbranchattentionnetworkfordetectingsmallobjectsinremotesensingimages AT kaiminsun noveldynamiccontextbranchattentionnetworkfordetectingsmallobjectsinremotesensingimages AT yepeichen noveldynamiccontextbranchattentionnetworkfordetectingsmallobjectsinremotesensingimages |