FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR
Addressing the challenges of small target detection in aerial infrared images from a drone’s perspective, such as diverse target scales, complex backgrounds, the clustering of small targets, and limited computational resources of the drone platform. This paper proposes a lightweight UAV i...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10836685/ |
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| author | Renzheng Xue Shijie Hua Haiqiang Xu |
| author_facet | Renzheng Xue Shijie Hua Haiqiang Xu |
| author_sort | Renzheng Xue |
| collection | DOAJ |
| description | Addressing the challenges of small target detection in aerial infrared images from a drone’s perspective, such as diverse target scales, complex backgrounds, the clustering of small targets, and limited computational resources of the drone platform. This paper proposes a lightweight UAV infrared small target detection algorithm, FECI-RTDETR. Initially, we introduce a lightweight RFConv-Block module that enhances spatial feature extraction capabilities while reducing computational redundancy. Subsequently, we combine the Efficient Additive feature selection mechanism with an intra-scale feature interaction module to form the EA-AIFI module, which strengthens the model’s focus on dense targets and reduces computational burden. Moreover, we introduce the CHS-FPN structure as a cross-scale feature fusion structure, utilizing the coordinate attention mechanism combined with a hierarchical scale-based feature pyramid network. This allows the model to better understand the contextual semantics of targets and improves detection accuracy. Finally, the original GIoU loss is replaced with Inner-GIoU loss, using a scaling factor to control the auxiliary enclosing box, which accelerates convergence speed and enhances detection accuracy for small targets. Experimental results indicate that compared to RT-DETR, the FECI-RTDETR model reduces the number of parameters by 24.56% and floating-point operations by 19.12% on the HIT-UAV dataset. The mAP50 and mAP50:95 metrics improved by 4.2% and 2.9%, respectively, with the mAP50 reaching 84.2%. This algorithmic model achieves a balance between resource reduction and accuracy enhancement while maintaining lightweight characteristics. |
| format | Article |
| id | doaj-art-708878656e2f447e80ff9575aaec1a0c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-708878656e2f447e80ff9575aaec1a0c2025-01-21T00:02:02ZengIEEEIEEE Access2169-35362025-01-01139578959110.1109/ACCESS.2025.352823710836685FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETRRenzheng Xue0https://orcid.org/0009-0000-7183-4502Shijie Hua1https://orcid.org/0009-0001-8926-6117Haiqiang Xu2https://orcid.org/0009-0008-2179-7684School of Computer and Control Engineering, Qiqihar University, Qiqihar, ChinaSchool of Computer and Control Engineering, Qiqihar University, Qiqihar, ChinaSchool of Computer and Control Engineering, Qiqihar University, Qiqihar, ChinaAddressing the challenges of small target detection in aerial infrared images from a drone’s perspective, such as diverse target scales, complex backgrounds, the clustering of small targets, and limited computational resources of the drone platform. This paper proposes a lightweight UAV infrared small target detection algorithm, FECI-RTDETR. Initially, we introduce a lightweight RFConv-Block module that enhances spatial feature extraction capabilities while reducing computational redundancy. Subsequently, we combine the Efficient Additive feature selection mechanism with an intra-scale feature interaction module to form the EA-AIFI module, which strengthens the model’s focus on dense targets and reduces computational burden. Moreover, we introduce the CHS-FPN structure as a cross-scale feature fusion structure, utilizing the coordinate attention mechanism combined with a hierarchical scale-based feature pyramid network. This allows the model to better understand the contextual semantics of targets and improves detection accuracy. Finally, the original GIoU loss is replaced with Inner-GIoU loss, using a scaling factor to control the auxiliary enclosing box, which accelerates convergence speed and enhances detection accuracy for small targets. Experimental results indicate that compared to RT-DETR, the FECI-RTDETR model reduces the number of parameters by 24.56% and floating-point operations by 19.12% on the HIT-UAV dataset. The mAP50 and mAP50:95 metrics improved by 4.2% and 2.9%, respectively, with the mAP50 reaching 84.2%. This algorithmic model achieves a balance between resource reduction and accuracy enhancement while maintaining lightweight characteristics.https://ieeexplore.ieee.org/document/10836685/Small target detectionRT-DETRlightweight structureUAVefficient additive |
| spellingShingle | Renzheng Xue Shijie Hua Haiqiang Xu FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR IEEE Access Small target detection RT-DETR lightweight structure UAV efficient additive |
| title | FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR |
| title_full | FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR |
| title_fullStr | FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR |
| title_full_unstemmed | FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR |
| title_short | FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR |
| title_sort | feci rtdetr a lightweight unmanned aerial vehicle infrared small target detector algorithm based on rt detr |
| topic | Small target detection RT-DETR lightweight structure UAV efficient additive |
| url | https://ieeexplore.ieee.org/document/10836685/ |
| work_keys_str_mv | AT renzhengxue fecirtdetralightweightunmannedaerialvehicleinfraredsmalltargetdetectoralgorithmbasedonrtdetr AT shijiehua fecirtdetralightweightunmannedaerialvehicleinfraredsmalltargetdetectoralgorithmbasedonrtdetr AT haiqiangxu fecirtdetralightweightunmannedaerialvehicleinfraredsmalltargetdetectoralgorithmbasedonrtdetr |