MAN and CAT: mix attention to nn and concatenate attention to YOLO

Guan, Runwei; Man, Ka Lok; Zhao, Haocheng; Zhang, Rui-Xiao; Yao, Shanliang; Smith, Jeremy S.; Lim, Eng Gee; Yue, Yutao

doi:10.1007/s11227-022-04726-7

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Hybrid attention mechanisms that combine spatial and channel dimensions also exist ( Zhang & Sabuncu, 2018 ). By integrating attention mechanisms into both spatial and channel dimensions, these models can simultaneously focus on different regions and feature channels within an image ( Guan et al, 2023 ). This comprehensive approach enables the model to capture important information in the image more effectively.…”

Section: Algorithm Design and Analysismentioning

confidence: 99%

A small target detection algorithm based on improved YOLOv5 in aerial image

Zhang,

Liu

2024

PeerJ Computer Science

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Uncrewed aerial vehicle (UAV) aerial photography technology is widely used in both industrial and military sectors, but remote sensing for small target detection still faces several challenges. Firstly, the small size of targets increases the difficulty of detection and recognition. Secondly, complex aerial environmental conditions, such as lighting changes and background noise, significantly affect the quality of detection. Rapid and accurate identification of target categories is also a key issue, requiring improvements in detection speed and accuracy. This study proposes an improved remote sensing target detection algorithm based on the YOLOv5 architecture. In the YOLOv5s model, the Distribution Focal Loss function is introduced to accelerate the convergence speed of the network and enhance the network’s focus on annotated data. Simultaneously, adjustments are made to the Cross Stage Partial (CSP) network structure, modifying the convolution kernel size, adding a new stack-separated convolution module, and designing a new attention mechanism to achieve effective feature fusion between different hierarchical structure feature maps. Experimental results demonstrate a significant performance improvement of the proposed algorithm on the RSOD dataset, with a 3.5% increase in detection accuracy compared to the original algorithm. These findings indicate that our algorithm effectively enhances the precision of remote sensing target detection and holds potential application prospects.

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Section: Algorithm Design and Analysismentioning

confidence: 99%

A small target detection algorithm based on improved YOLOv5 in aerial image

Zhang,

Liu

2024

PeerJ Computer Science

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“…We use different attention modules in the network to compare their performance in lead detection. Mix attention (MA) [38] is one of the latest attention modules based on lightweight CNN and ViT; it combines channel, spatial, and global context attention while enhancing both the feature representation of the target itself and the correlation between targets. Furthermore, the convolutional block attention module (CBAM) [39] is a highly cited module that combines channel attention and spatial attention.…”

Section: Different Attention Modulesmentioning

confidence: 99%

A Shape-Aware Network for Arctic Lead Detection from Sentinel-1 SAR Images

Song,

Zhu,

et al. 2024

JMSE

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Accurate detection of sea ice leads is essential for safe navigation in polar regions. In this paper, a shape-aware (SA) network, SA-DeepLabv3+, is proposed for automatic lead detection from synthetic aperture radar (SAR) images. Considering the fact that training data are limited in the task of lead detection, we construct a dataset fusing dual-polarized (HH, HV) SAR images from the C-band Sentinel-1 satellite. Taking the DeepLabv3+ as the baseline network, we introduce a shape-aware module (SAM) to combine multi-scale semantic features and shape information and, therefore, better capture the shape characteristics of leads. A squeeze-and-excitation channel-position attention module (SECPAM) is designed to enhance lead feature extraction. Segmentation loss generated by the segmentation network and shape loss generated by the shape-aware stream are combined to optimize the network during training. Postprocessing is performed to filter out segmentation errors based on the aspect ratio of leads. Experimental results show that the proposed method outperforms the existing benchmarking deep learning methods, reaching 96.82% for overall accuracy, 93.01% for F1-score, and 91.48% for mIoU. It is also found that the fusion of dual-polarimetric SAR channels as the input could effectively improve the accuracy of sea ice lead detection.

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