MashFormer: A Novel Multiscale Aware Hybrid Detector for Remote Sensing Object Detection

Wang, Keyan; Bai, Feiyu; Li, Jiaojiao; Liu, Yajing; Li, Yunsong

doi:10.1109/jstars.2023.3254047

Cited by 11 publications

(4 citation statements)

References 50 publications

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“…To address these complexities, researchers have introduced innovative techniques. Mashformer [63] presents a hybrid detector that integrates multi-scale perception convolutional neural networks (CNN) and Transformers. This integration captures relationships between remote features, thereby enhancing expressiveness in complex background scenarios and improving target detection across different scales.…”

Section: Object Detection Based On Deep Learning Methods In Remote Se...mentioning

confidence: 99%

Faster and Lightweight: An Improved YOLOv5 Object Detector for Remote Sensing Images

Zhang,

Chen,

Yan

et al. 2023

Remote Sensing

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In recent years, the realm of deep learning has witnessed significant advancements, particularly in object detection algorithms. However, the unique challenges posed by remote sensing images, such as complex backgrounds, diverse target sizes, dense target distribution, and overlapping or obscuring targets, demand specialized solutions. Addressing these challenges, we introduce a novel lightweight object detection algorithm based on Yolov5s to enhance detection performance while ensuring rapid processing and broad applicability. Our primary contributions include: firstly, we implemented a new Lightweight Asymmetric Detection Head (LADH-Head), replacing the original detection head in the Yolov5s model. Secondly, we introduce a new C3CA module, incorporating the Coordinate Attention mechanism, strengthening the network’s capability to extract precise location information. Thirdly, we proposed a new backbone network, replacing the C3 module in the Yolov5s backbone with a FasterConv module, enhancing the network’s feature extraction capabilities. Additionally, we introduced a Content-aware Feature Reassembly (content-aware reassembly of features) (CARAFE) module to reassemble semantic similar feature points effectively, enhancing the network’s detection capabilities and reducing the model parameters. Finally, we introduced a novel XIoU loss function, aiming to improve the model’s convergence speed and robustness during training. Experimental results on widely used remote sensing image datasets such as DIOR, DOTA, and SIMD demonstrate the effectiveness of our proposed model. Compared to the original Yolov5s algorithm, we achieved a mean average precision (mAP) increase of 3.3%, 6.7%, and 3.2%, respectively. These findings underscore the superior performance of our proposed model in remote sensing image object detection, offering an efficient, lightweight solution for remote sensing applications.

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Section: Object Detection Based On Deep Learning Methods In Remote Se...mentioning

confidence: 99%

Faster and Lightweight: An Improved YOLOv5 Object Detector for Remote Sensing Images

Zhang,

Chen,

Yan

et al. 2023

Remote Sensing

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“…To address these issues, many researchers have conducted innovative studies. Wang et al [33] introduced MashFormer for improved small target detection through feature alignment. Hao et al [34] explored data augmentation to address model overfitting and sample scarcity.…”

Section: A Detection Of Small Targets In Satellite Remote Sensing Ima...mentioning

confidence: 99%

SatDetX-YOLO: A More Accurate Method for Vehicle Target Detection in Satellite Remote Sensing Imagery

Zhao,

Guo,

Shao

et al. 2024

IEEE Access

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Satellite remote sensing technology significantly contributes to intelligent transportation by optimizing traffic planning via global perspectives and rich data, enhancing traffic efficiency and reducing environmental impact. However, current target detection models frequently exhibit low accuracy in vehicle detection tasks due to complex background interference in satellite imageries and a need for critical semantic information. To improve vehicle target detection accuracy, this study introduces SatDetX-YOLO, a vehicle detection model for satellite remote sensing images based on YOLOv8. The model involves reconstructing the backbone network with FasterNet for enhanced feature extraction, a redesigned decoupled head for improved computational efficiency and complex data processing, and incorporating the Deformable Attention Module (DAM) to increase sensitivity to small targets and feature correlation capture. Employing the Maximum Probabilistic Distance IoU (MPDIoU) loss function enhances adaptability and generalization to diverse vehicle targets. Experimental results demonstrate that under comparable FPS, SatDetX-YOLO's Precision (P), Recall (R), and Mean Average Precision (mAP) improved by 3.5%, 3.3%, and 3.2%, respectively. Despite a minor reduction in FPS, the model significantly enhances detection accuracy, striking a balance between accuracy and speed.

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“…Researchers have also proposed innovative methods to reason about both single-temporal and cross-temporal semantic correlations for change detection [173], and spatial-spectral cross fusion approaches such as SSCFNet [178] have been introduced to improve change detection performance. Furthermore, multi-scale geometric techniques like Shearlet [175] and contourlet [169] have been applied to change detection to provide multi-scale and multi-directional features, leading to better performance in detecting changes in remote sensing images.…”

Section: Applications Of Rs Foundation Modelmentioning

confidence: 99%

Brain-Inspired Remote Sensing Foundation Models and Open Problems: A Comprehensive Survey

Jiao,

Huang,

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The foundation model (FM) has garnered significant attention for its remarkable transfer performance in downstream tasks. Typically, it undergoes task-agnostic pre-training on a large dataset and can be efficiently adapted to various downstream applications through fine-tuning. While FMs have been extensively explored in language and other domains, their potential in remote sensing has also begun to attract scholarly interest. However, comprehensive investigations and performance comparisons of these models on remote sensing tasks are currently lacking. In this survey, we provide essential background knowledge by introducing key technologies and recent developments in FMs. Subsequently, we explore essential downstream applications in remote sensing, covering classification, localization, and understanding. Our analysis encompasses over thirty FMs in both natural and remote sensing fields, and we conduct extensive experiments on more than ten datasets, evaluating global feature representation, local feature representation, and target localization. Through quantitative assessments, we highlight the distinctions among various foundation models and confirm that pre-trained largescale natural FMs can also deliver outstanding performance in remote sensing tasks. After that, we systematically presented a brain-inspired framework for remote sensing foundation models (RSFMs). We delve into the brain-inspired characteristics in this framework, including structure, perception, learning, and cognition. To conclude, we summarize twelve open problems in RSFMs, providing potential research directions. Our survey offers valuable insights into the burgeoning field of RSFMs and aims to foster further advancements in this exciting area.

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MashFormer: A Novel Multiscale Aware Hybrid Detector for Remote Sensing Object Detection

Cited by 11 publications

References 50 publications

Faster and Lightweight: An Improved YOLOv5 Object Detector for Remote Sensing Images

Faster and Lightweight: An Improved YOLOv5 Object Detector for Remote Sensing Images

SatDetX-YOLO: A More Accurate Method for Vehicle Target Detection in Satellite Remote Sensing Imagery

Brain-Inspired Remote Sensing Foundation Models and Open Problems: A Comprehensive Survey

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