FEA-Swin: Foreground Enhancement Attention Swin Transformer Network for Accurate UAV-Based Dense Object Detection

Xu, Wenyu; Zhang, Chaofan; Wang, Qi; Dai, Pangda

doi:10.3390/s22186993

Cited by 16 publications

(7 citation statements)

References 45 publications

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“…The Swin Transformer [ 33 ] module consists of a multi-layer perceptron, a Window Multi-head Self-Attention (WMSA), a Shifted Window-based Multi-head Self Attention (SWMSA), and a Layer Normalization (LN). The workflow is shown as Figure 2 .…”

Section: Related Workmentioning

confidence: 99%

Multiple Attention Mechanism Enhanced YOLOX for Remote Sensing Object Detection

Shen

Gao

2023

Sensors

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The object detection technologies of remote sensing are widely used in various fields, such as environmental monitoring, geological disaster investigation, urban planning, and military defense. However, the detection algorithms lack the robustness to detect tiny objects against complex backgrounds. In this paper, we propose a Multiple Attention Mechanism Enhanced YOLOX (MAME-YOLOX) algorithm to address the above problem. Firstly, the CBAM attention mechanism is introduced into the backbone of the YOLOX, so that the detection network can focus on the saliency information. Secondly, to identify the high-level semantic information and enhance the perception of local geometric feature information, the Swin Transformer is integrated into the YOLOX’s neck module. Finally, instead of GIOU loss, CIoU loss is adopted to measure the bounding box regression loss, which can prevent the GIoU from degenerating into IoU. The experimental results of three publicly available remote sensing datasets, namely, AIBD, HRRSD, and DIOR, show that the algorithm proposed possesses better performance, both in relation to quantitative and qualitative aspects.

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Section: Related Workmentioning

confidence: 99%

Multiple Attention Mechanism Enhanced YOLOX for Remote Sensing Object Detection

Shen

Gao

2023

Sensors

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“…This innovation was named Transformer Enhanced FPN (TEF) module. In another study, Xu et al [48] developed a weighted Bidirectional Feature Pyramid Network (BiFPN) through the integration of skip connection operations with the Swin transformer. This approach effectively preserved information pertinent to small objects.…”

Section: Fast Attention For High-resolution or Multi-scale Feature Mapsmentioning

confidence: 99%

“…Moreover, DESTR uses a mini-detector to ensure proper content query initialization in the decoder and enhances the self-attention module. Another research [48], introduced FEA-Swin, which leverages advanced foreground Fig. 9: DAB-DETR improves Conditional DETR and utilizes dynamic anchor boxes to sequentially provide better reference query points and anchor sizes (figure from [67]).…”

Section: Architecture and Block Modificationsmentioning

confidence: 99%

Extended Expectation Maximization for Under-Fitted Models

Rekavandi¹,

SEGHOUANE²,

Boussaid³

et al. 2022

Preprint

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<p>In this paper, we generalize the well-known Expectation Maximization (EM) algorithm using the α− divergence for Gaussian Mixture Model (GMM). This approach is used in robust subspace detection when the number of parameters is kept small to avoid overfitting and large estimation variances. The level of robustness can be tuned by the parameterα. When α→1, our method is equivalent to the standard EM approach and for α <1 the method is robust against potential outliers. Simulation results show that the method outperforms the standard EM when it comes to mismatches between noise models and their realizations. In addition, we use the proposed method to detect active brain areas using collected functional Magnetic Resonance Imaging (fMRI) data during task-related experiments.</p>

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“…(2) Influence of M-ASFF In this section, we explore the impact of micro adaptive feature fusion (M-ASFF) on the model. Since the main goal of M-ASFF is to achieve adaptive fusion of features at different scales, we selected comparative models of different feature fusion methods, mainly including YOLOv3 using only the FPN structure, the YOLOv5s source model using FPN+PANet, and a combination of The Swin Transformer's Weighted Bidirectional Feature Pyramid Network (TBIFPN) [31]. From Table 6, we can conclude that the YOLOv5s model with the addition of the M-ASFF module performs the best on the rail surface defect dataset.…”

Section: Ablation Experimentsmentioning

confidence: 99%

“…In [30], the researchers used the fuzzy C-means algorithm to re-cluster the anchor boxes based on YOLOv4 and added a shallow feature layer to solve the problem of occlusion of hanging insulators and power components. In [31], contextual information is integrated into the backbone of the Swin Transformer, and skip-connected BiFPN is used to improve detection of small objects.…”

Section: Introductionmentioning

confidence: 99%

Detection of Surface Defects on Railway Tracks Based on Deep Learning

Wang

Zhu

et al. 2022

IEEE Access

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The detection of rail surface defects is very important in railway transportation. However, the edge defects on both sides of the rail and the multi-scale variation between different types of defects both pose challenges to the detection of rail surface defects. In order to solve the above problems, this paper proposes a novel rail surface defect detection network, YOLOv5s-VF. First, we design a sharpening functional attention mechanism (V-CBAM) that contains two key components: adaptive channel attention (F-CAM) and sharpened spatial attention (SSA). In F-CAM, we use one-dimensional convolution with adaptive convolution kernels for cross-channel connections, which reduces the number of parameters of the attention mechanism without affecting its performance. In SSA, we design a sharpening filter suitable for spatial attention, which is used to enhance the attention to the edge position defects of railway tracks and enhance the detection effect of the network on edge defects. Second, we construct a microscale adaptive spatial feature fusion (M-ASFF), which adds a high-resolution feature extraction layer to enhance the details of the underlying features of tiny defects. At the same time, in order to prevent the loss of detailed information and the excessive increase of the parameters of the model, the low-resolution feature layer is removed. Combined with adaptive spatial feature fusion, it can prevent the semantic conflict caused by the fusion of features at different scales. Finally, given the lack of labeled public rail surface defect datasets, this paper is based on the collection of real rail images and manually labels defects to train an object detection network and open source it. The experimental results show that YOLOv5s-VF outperforms the existing rail surface defect detection methods with a detection accuracy of 93.5% and a detection speed of 114.9 fps.

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FEA-Swin: Foreground Enhancement Attention Swin Transformer Network for Accurate UAV-Based Dense Object Detection

Cited by 16 publications

References 45 publications

Multiple Attention Mechanism Enhanced YOLOX for Remote Sensing Object Detection

Multiple Attention Mechanism Enhanced YOLOX for Remote Sensing Object Detection

Extended Expectation Maximization for Under-Fitted Models

Detection of Surface Defects on Railway Tracks Based on Deep Learning

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