Lightweight faster R-CNN for object detection in optical remote sensing images
Abstract Various applications in remote sensing rely on object detection approaches, such as urban detection, precision farming, and disaster prediction. Faster RCNN has gained popularity for its performance but comes with significant computational and storage demands. Model compression techniques l...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99242-y |
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| author | Andrew Magdy Marwa S. Moustafa Hala M. Ebied Mohamed F. Tolba |
| author_facet | Andrew Magdy Marwa S. Moustafa Hala M. Ebied Mohamed F. Tolba |
| author_sort | Andrew Magdy |
| collection | DOAJ |
| description | Abstract Various applications in remote sensing rely on object detection approaches, such as urban detection, precision farming, and disaster prediction. Faster RCNN has gained popularity for its performance but comes with significant computational and storage demands. Model compression techniques like pruning and quantization are frequently employed to mitigate these challenges. This paper introduces a novel bi-stage compression approach to create a lightweight Faster R-CNN for satellite images with minimal performance degradation. The proposed approach employs two distinct phases: aware training and post-training compression. First, aware training employs mixed-precision FP16 computation, which enhances training speed by a factor of 1.5 to 5.5 while preserving model accuracy and optimizing memory efficiency. Second, post-training compression applies unstructured weight pruning to eliminate redundant parameters, followed by dynamic quantization to reduce precision, thereby minimizing the model size at runtime and computational load. The proposed approach was assessed on the NWPU VHR-10 and Ship datasets. The results demonstrate an average 25.6% reduction in model size and a 56.6% reduction in parameters while maintaining the mean Average Precision (mAP). |
| format | Article |
| id | doaj-art-bc91c387203543f997d6c3373dccabb6 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bc91c387203543f997d6c3373dccabb62025-08-20T02:15:07ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-99242-yLightweight faster R-CNN for object detection in optical remote sensing imagesAndrew Magdy0Marwa S. Moustafa1Hala M. Ebied2Mohamed F. Tolba3Department of Scientific Computing, Faculty of Computer and Information Sciences, Ain Shams UniversityDepartment of Image Processing and Its Application, National Authority for Remote Sensing and Space Sciences (NARSS)Department of Scientific Computing, Faculty of Computer and Information Sciences, Ain Shams UniversityDepartment of Scientific Computing, Faculty of Computer and Information Sciences, Ain Shams UniversityAbstract Various applications in remote sensing rely on object detection approaches, such as urban detection, precision farming, and disaster prediction. Faster RCNN has gained popularity for its performance but comes with significant computational and storage demands. Model compression techniques like pruning and quantization are frequently employed to mitigate these challenges. This paper introduces a novel bi-stage compression approach to create a lightweight Faster R-CNN for satellite images with minimal performance degradation. The proposed approach employs two distinct phases: aware training and post-training compression. First, aware training employs mixed-precision FP16 computation, which enhances training speed by a factor of 1.5 to 5.5 while preserving model accuracy and optimizing memory efficiency. Second, post-training compression applies unstructured weight pruning to eliminate redundant parameters, followed by dynamic quantization to reduce precision, thereby minimizing the model size at runtime and computational load. The proposed approach was assessed on the NWPU VHR-10 and Ship datasets. The results demonstrate an average 25.6% reduction in model size and a 56.6% reduction in parameters while maintaining the mean Average Precision (mAP).https://doi.org/10.1038/s41598-025-99242-yFaster R-CNNPruningQuantization |
| spellingShingle | Andrew Magdy Marwa S. Moustafa Hala M. Ebied Mohamed F. Tolba Lightweight faster R-CNN for object detection in optical remote sensing images Scientific Reports Faster R-CNN Pruning Quantization |
| title | Lightweight faster R-CNN for object detection in optical remote sensing images |
| title_full | Lightweight faster R-CNN for object detection in optical remote sensing images |
| title_fullStr | Lightweight faster R-CNN for object detection in optical remote sensing images |
| title_full_unstemmed | Lightweight faster R-CNN for object detection in optical remote sensing images |
| title_short | Lightweight faster R-CNN for object detection in optical remote sensing images |
| title_sort | lightweight faster r cnn for object detection in optical remote sensing images |
| topic | Faster R-CNN Pruning Quantization |
| url | https://doi.org/10.1038/s41598-025-99242-y |
| work_keys_str_mv | AT andrewmagdy lightweightfasterrcnnforobjectdetectioninopticalremotesensingimages AT marwasmoustafa lightweightfasterrcnnforobjectdetectioninopticalremotesensingimages AT halamebied lightweightfasterrcnnforobjectdetectioninopticalremotesensingimages AT mohamedftolba lightweightfasterrcnnforobjectdetectioninopticalremotesensingimages |