Res-SwinTransformer with Local Contrast Attention for Infrared Small Target Detection

Zhao, Tianhua; Cao, Jie; Hao, Qun; Bao, Chun; Shi, Moudan

doi:10.3390/rs15184387

Cited by 4 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CSWin Transformer [27] and CrossFormer [40] modified the window shape to axial strips and dilated windows, respectively. Zhao [41] designed Res-SwinTransformer with a Local Contrast Attention Network named RSLCANet for infrared small-target detection in aerial remote sensing. With fewer parameters, RSLCANet was suitable for practical deployment in applications like car navigation, crop monitoring, and infrared warning.…”

Section: Transformer-based Detectorsmentioning

confidence: 99%

Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective

Zeng,

Gao,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Small-scale low-altitude unmanned aerial vehicles (UAVs) equipped with perception capability for military targets will become increasingly essential for strategic reconnaissance and stationary patrols in the future. To respond to challenges such as complex terrain and weather variations, as well as the deception and camouflage of military targets, this paper proposes a hybrid detection model that combines Convolutional Neural Network (CNN) and Transformer architecture in a decoupled manner. The proposed detector consists of the C-branch and the T-branch. In the C-branch, Multi-gradient Path Network (MgpNet) is introduced, inspired by the multi-gradient flow strategy, excelling in capturing the local feature information of an image. In the T-branch, RPFormer, a Region–Pixel two-stage attention mechanism, is proposed to aggregate the global feature information of the whole image. A feature fusion strategy is proposed to merge the feature layers of the two branches, further improving the detection accuracy. Furthermore, to better simulate real UAVs’ reconnaissance environments, we construct a dataset of military targets in complex environments captured from an oblique perspective to evaluate the proposed detector. In ablation experiments, different fusion methods are validated, and the results demonstrate the effectiveness of the proposed fusion strategy. In comparative experiments, the proposed detector outperforms most advanced general detectors.

show abstract

Section: Transformer-based Detectorsmentioning

confidence: 99%

Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective

Zeng,

Gao,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…This model adeptly captures global information and essential visual features, boosting the ability to recognize tiny defects. Zhao et al [41] addressed the small-sized, low signal-to-noise ratio and texture-detail-scarce targets by proposing a Res-SwinTransformer with a Local Contrastive Attention Network (RSLCANet). Experimental results showcase low false detection rates, high accuracy, and fast detection speed.…”

Section: Related Workmentioning

confidence: 99%

Insulator Defect Detection Based on YOLOv8s-SwinT

He,

Yang,

Liu

et al. 2024

Information

View full text Add to dashboard Cite

Ensuring the safety of transmission lines necessitates effective insulator defect detection. Traditional methods often need more efficiency and accuracy, particularly for tiny defects. This paper proposes an innovative insulator defect recognition method leveraging YOLOv8s-SwinT. Combining Swin Transformer and Convolutional Neural Network (CNN) enhances the model’s understanding of multi-scale global semantic information through cross-layer interactions. The improved BiFPN structure in the neck achieves bidirectional cross-scale connections and weighted feature fusion during feature extraction. Additionally, a new small-target detection layer enhances the capability to detect tiny defects. The experimental results showcase outstanding performance, with precision, recall, and mAP reaching 95.6%, 95.3%, and 97.7%, respectively. This boosts detection efficiency and ensures high accuracy, providing robust support for real-time detection of tiny insulator defects.

show abstract

“…Zhangu et al 29 fused radar and infrared cameras to greatly enrich the completeness of vehicle cognitive information and then proposed an attention mechanism based on radar guidance information to extract the vehicle ROI. Zhao et al 30 introduced a residual structure to fully retain the shallow detail information of small infrared target vehicles.…”

Section: Introductionmentioning

confidence: 99%

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

Zhang,

2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

Vehicle detection using aerial thermal infrared images has received significant attention because of its strong capability for day and night observations to supply information for vehicle tracking, traffic monitoring, and road network planning. Compared with aerial visible images, aerial thermal infrared images are not sensitive to lighting conditions. However, they have low contrast and blurred edges. Therefore, a combinational and sparse you-only-look-once (ComS-YOLO) neural network is put forward to accurately and quickly detect vehicles in aerial thermal infrared images. Therein, we adjust the structure of the deep neural network to balance the detection accuracy and running time. In addition, we propose an objective function that utilizes the diagonal distance of the corresponding minimum external rectangle, which prevents non-convergence when there is an inclusion relationship between the prediction and true boxes or in the case of width and height alignment. Furthermore, to avoid over-fitting in the training stage, we eliminate some redundant parameters via constraints and on-line pruning. Finally, experimental results on the NWPU VHR-10 and DARPA VIVID datasets show that the proposed ComS-YOLO network effectively and efficiently identifies the vehicles with a low missed rate and false detection rate. Compared with the Faster R-CNN and a series of YOLO neural networks, the proposed neural network presents satisfactory and competitive results in terms of the detection accuracy and running time. Furthermore, vehicle detection experiments under different environments are also carried out, which shows that our method can achieve an excellent and desired performance on detection accuracy and robustness of vehicle detection.

show abstract

Res-SwinTransformer with Local Contrast Attention for Infrared Small Target Detection

Cited by 4 publications

References 47 publications

Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective

Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective

Insulator Defect Detection Based on YOLOv8s-SwinT

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

Contact Info

Product

Resources

About