DFA-Net: Multi-Scale Dense Feature-Aware Network via Integrated Attention for Unmanned Aerial Vehicle Infrared and Visible Image Fusion

Shen, Sen; Li, Di; Mei, Liye; Xu, Chuan; Ye, Zhaoyi; Zhang, Qi; Hong, Bin Na; Yang, Wei; Wang, Ying

doi:10.3390/drones7080517

Cited by 4 publications

(1 citation statement)

References 58 publications

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“…Because the infrared remote sensing vehicles are small and weak, the current vehicle detection studies focus on feature fusion 24,25 and multi-scale detection 26 to keep the small-scale features. For instance, Du et al 27 proposed the focus and attention mechanism-based YOLO (FA-YOLO) to detect the infrared occluded vehicles in the complex background of remote sensing images.…”

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.

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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.

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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.

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GTMFuse: Group-attention transformer-driven multiscale dense feature-enhanced network for infrared and visible image fusion

Mei,

Hu,

et al. 2024

Knowledge-Based Systems

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ADF‐Net: Attention‐guided deep feature decomposition network for infrared and visible image fusion

Shen,

Zhang,

Dong

et al. 2024

IET Image Processing

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To effectively enhance the ability to acquire information by making full use of the complementary features of infrared and visible images, the widely used image fusion algorithm is faced with challenges such as information loss and image blurring. In response to this issue, the authors propose a dual‐branch deep hierarchical fusion network (ADF‐Net) guided by an attention mechanism. Initially, the attention convolution module extracts the shallow features of the image. Subsequently, a dual‐branch deep decomposition feature extractor is introduced, where in the transformer encoder block (TEB) employs remote attention to process low‐frequency global features, while the CNN encoder block (CEB) extracts high‐frequency local information. Ultimately, the global fusion layer based on TEB and the local fusion layer based on CEB produce the fused image through the encoder. Multiple experiments demonstrate that ADF‐Net excels in various aspects by utilizing two‐stage training and an appropriate loss function for training and testing.

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DFA-Net: Multi-Scale Dense Feature-Aware Network via Integrated Attention for Unmanned Aerial Vehicle Infrared and Visible Image Fusion

Cited by 4 publications

References 58 publications

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

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

GTMFuse: Group-attention transformer-driven multiscale dense feature-enhanced network for infrared and visible image fusion

ADF‐Net: Attention‐guided deep feature decomposition network for infrared and visible image fusion

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