Infrared small target detection techniques remain a challenging task due to the complex background. To overcome this problem, by exploring context information, this research presents a data-driven approach called atte...
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Infrared small target detection techniques remain a challenging task due to the complex background. To overcome this problem, by exploring context information, this research presents a data-driven approach called attention-guided pyramid context network (AGPCNet). Specifically, we design attention-guided context block and perceive pixel correlations within and between patches at specific scales via local semantic association and global context attention, respectively. Then, the contextual information from multiple scales is fused by context pyramid module to achieve better feature representation. In the upsampling stage, we fuse the low and deep semantics through asymmetric fusion module to retain more information about small targets. The experimental results illustrate that AGPCNet has achieved state-of-the-art performance on three available infrared small target datasets.
Lightweight face detection algorithms that typically utilize convolutional neural network to find out all faces from the entire vision range. However, compared with more accurate and heavy algorithms, the performance ...
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Lightweight face detection algorithms that typically utilize convolutional neural network to find out all faces from the entire vision range. However, compared with more accurate and heavy algorithms, the performance of existing lightweight networks is still left far behind. Toward this end, we propose a lightweight and efficient single-stage face detector, named ACWFace, which explores the effects of attention, context module, and weighted feature fusion based on RetinaFace. First, efficient dual attention module is designed to further explore the potential of channel attention and spatial attention by introducing adaptive convolution kernel. Second, extended context module and shuffled context module are proposed to enlarge the receptive field and increase the information intersection between branches. Finally, weighted-fusion feature pyramid network is utilized to solve the features fusion of different scales equally by introducing the feature fusion module. Experiments on the easy, medium, and hard datasets of WIDER FACE validation partition show that our ACWFace outperforms RetinaFace average precision by 1.0%, 1.1%, and 1.8% while it achieves a great growth of 0.6%, 6.5%, and 3.0% on annotated faces in the wild, PASCAL face, and face detection data set and benchmark datasets, respectively. (C) 2022 SPIE and IS&T
This study proposes a novel face detector called DEFace that focuses on the challenging tasks of face detection to cope with a small size that is under 12 pixels and occlusions due to a mask or human body parts. This ...
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This study proposes a novel face detector called DEFace that focuses on the challenging tasks of face detection to cope with a small size that is under 12 pixels and occlusions due to a mask or human body parts. This study proposed the extended feature pyramid network (FPN) module to detect small faces by expanding the range of P layer, and the network by adding a receptive context module (RCM) after each predicted feature head from the top-down pathway in the FPN architecture to enhance the feature discriminability and the robustness. Based on the FPN principle, the combination between the low- and high-resolutions are beneficial for object detection with different object sizes. Furthermore, with assistance from the RCM, the proposed method can use a broad range of context information especially for small faces. To evaluate the performance of the proposed method, various public face datasets are used such as the WIDER Face dataset, the face detection dataset and benchmark (FDDB), and the masked faces (MAFA) dataset, which consist of challenging samples such as small face regions and occlusions by hair or other people. The results indicate that DEFace can detect the face region more accurately in comparison to the other state-of-the-art methods while maintaining the processing time.
Accurate multiclass object detection in remote sensing images is a challenging task, especially for small objects. Since the scales of objects in remote sensing images have a great variance, almost all of the advanced...
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ISBN:
(纸本)9781538691540
Accurate multiclass object detection in remote sensing images is a challenging task, especially for small objects. Since the scales of objects in remote sensing images have a great variance, almost all of the advanced detection methods have shortcomings. Consequently, improving the accuracy of multiclass objects detection has always been the direction of researchers' efforts. In this paper, a spatial enhanced-Single Shot MultiBox Detector (SE-SSD) is proposed. First, to enhance the spatial information, we enlarge the input image channels with embedding oriented-gradients feature maps. Second, the multiple output layers in the backbone network are changed to reduce one pooling operation. Finally, we design a context module to enhance the receptive field for feature layer description in SE-SSD framework. Experimental results on DOTA dataset demonstrate that Spatial Enhanced-SSD method reaches a much higher mean average precision (mAP) than Faster R-CNN, SSD and other classic detection network.
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