SDANet: Semantic-Embedded Density Adaptive Network for Moving Vehicle Detection in Satellite Videos

Feng, Jie; Liang, Yuping; Zhang, Xiangrong; Zhang, Junpeng; Jiao, Licheng

doi:10.1109/tip.2023.3251026

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…Then, the OICR uses K ICR branches to continuously refine the CSs. The final detection results are obtained through non-maximum suppression (NMS) [45][46][47][48][49] in terms of the CSs.…”

Section: Weakly Supervised Deep Detection Networkmentioning

confidence: 99%

SAM-Induced Pseudo Fully Supervised Learning for Weakly Supervised Object Detection in Remote Sensing Images

Qian,

Lin,

Chen

et al. 2024

Remote Sensing

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Weakly supervised object detection (WSOD) in remote sensing images (RSIs) aims to detect high-value targets by solely utilizing image-level category labels; however, two problems have not been well addressed by existing methods. Firstly, the seed instances (SIs) are mined solely relying on the category score (CS) of each proposal, which is inclined to concentrate on the most salient parts of the object; furthermore, they are unreliable because the robustness of the CS is not sufficient due to the fact that the inter-category similarity and intra-category diversity are more serious in RSIs. Secondly, the localization accuracy is limited by the proposals generated by the selective search or edge box algorithm. To address the first problem, a segment anything model (SAM)-induced seed instance-mining (SSIM) module is proposed, which mines the SIs according to the object quality score, which indicates the comprehensive characteristic of the category and the completeness of the object. To handle the second problem, a SAM-based pseudo-ground truth-mining (SPGTM) module is proposed to mine the pseudo-ground truth (PGT) instances, for which the localization is more accurate than traditional proposals by fully making use of the advantages of SAM, and the object-detection heads are trained by the PGT instances in a fully supervised manner. The ablation studies show the effectiveness of the SSIM and SPGTM modules. Comprehensive comparisons with 15 WSOD methods demonstrate the superiority of our method on two RSI datasets.

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“…Then, the OICR uses K ICR branches to continuously refine the CSs. The final detection results are obtained through non-maximum suppression (NMS) [45][46][47][48][49] in terms of the CSs.…”

Section: Weakly Supervised Deep Detection Networkmentioning

confidence: 99%

SAM-Induced Pseudo Fully Supervised Learning for Weakly Supervised Object Detection in Remote Sensing Images

Qian,

Lin,

Chen

et al. 2024

Remote Sensing

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“…Li et al [13] employed prototype contrast learning for fine-grained vehicle detection. Feng et al [14] designed SDANet to enhance vehicle detection in complex settings. Adekanmi et al [15] provided a comprehensive analysis of advanced target detection methods in satellite remote sensing.…”

Section: Introductionmentioning

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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“…To balance the detection accuracy and efficiency, an efficient bidirectional feature pyramid network neck is introduced. A novel semantic embedding density adaptive network (SDANet) was proposed in [16], which designs a new density matching algorithm to obtain each object by partitioning the clustering proposal and performing hierarchical and recursive matching of the corresponding centres. Ye et al [17] proposed a backbone network utilizing involution and self-attention, capable of extracting effective features from complex objects.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Detecting Dense and Small Objects in UAV Images

Li,

Zhou,

et al. 2024

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

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Object detection in UAV images is an important and challenging task for many applications, which often needs highly efficient detection algorithms to meet the accuracy and real-time requirements of the applications. In this paper, we investigate efficient mechanisms for detecting dense and small objects in UAV images. Specifically, 1) kernel K-means is used to obtain optimal anchors for dense and small object detection; 2) a spatial information enhancement module (SIE) is proposed to improve the detection accuracy of dense objects by extracting object spatial location information; 3) a Coord_C3 module is proposed to improve the receptive field of the network and to reduce the number of network parameters; 4) a small detection head is added in the Head of network and skip connections are employed in the Neck of network to improve the detection accuracy of small objects. Experimental results on the VisDrone-2019, LEVIR-ship and Stanford Drone datasets show that our method not only has higher detection accuracy, but also runs faster compared to state-of-the-art detection methods.

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SDANet: Semantic-Embedded Density Adaptive Network for Moving Vehicle Detection in Satellite Videos

Cited by 7 publications

References 33 publications

SAM-Induced Pseudo Fully Supervised Learning for Weakly Supervised Object Detection in Remote Sensing Images

SAM-Induced Pseudo Fully Supervised Learning for Weakly Supervised Object Detection in Remote Sensing Images

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

An Efficient Method for Detecting Dense and Small Objects in UAV Images

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