Dilated residual attention network for load disaggregation

Xia, Min; Liu, Wan’an; Xu, Yiqing; Wang, Ke; Zhang, Xu

doi:10.1007/s00521-019-04414-3

Cited by 41 publications

(16 citation statements)

References 39 publications

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“…This method protects information integrity by bypassing the input to the output directly. The residual block in ResNet-50 only needs to learn the difference between the input and output; this setup simplifies the learning objectives and reduces the difficulties, and thus solves the degradation problem during training process [28]. The residual block is especially suitable for small and medium-sized object recognitions in water body remote sensing images.…”

Section: Name Kernel Size Stride Dilated Rate Output Sizementioning

confidence: 99%

Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

Weng

Xia

et al. 2020

IJGI

Self Cite

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Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.

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Section: Name Kernel Size Stride Dilated Rate Output Sizementioning

confidence: 99%

Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

Weng

Xia

et al. 2020

IJGI

Self Cite

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“…Although CNNs have achieved great performance in image processing [21][22][23], traditional CNNs consist of fully connected layers, maximum or average poolings, and convolutional layers to capture only first-order information [24]. We believe that second-order statistics is more suitable to capture such baby's expression distortions than first-order statistics.…”

Section: Feature Guided Cnnmentioning

confidence: 99%

Feature Guided CNN for Baby’s Facial Expression Recognition

Lin

Jiang

2020

Complexity

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State-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks (CNNs). However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from a baby’s face image and how difficult is it? In this paper, we first introduce a new face image database, named BabyExp, which contains 12,000 images from babies younger than two years old, and each image is with one of three facial expressions (i.e., happy, sad, and normal). To the best of our knowledge, the proposed dataset is the first baby face dataset for analyzing a baby’s face image, which is complementary to the existing adult face datasets and can shed some light on exploring baby face analysis. We also propose a feature guided CNN method with a new loss function, called distance loss, to optimize interclass distance. In order to facilitate further research, we provide the benchmark of expression recognition on the BabyExp dataset. Experimental results show that the proposed network achieves the recognition accuracy of 87.90% on BabyExp.

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“…Autonomous detection and fishing by underwater robots will be the main way to obtain aquatic products in the future, and sea urchins are the main research object of aquatic product detection. To complete the autonomous detection and salvage of underwater robots [1], a series of basic research and scientific problems such as underwater communication [2], underwater positioning [3], information perception [4], target detection and identification [5], and target grasping need to be solved [6], which is an important field of concern for many researchers. Sea urchins are one of the mainstream objects in the current research of aquatic product detection.…”

Section: Introductionmentioning

confidence: 99%

“…e joint effect of local connection and parameter sharing is to reduce the number of parameters, make the operation simple and efficient, and be able to operate on very large data sets. Pooling is to aggregate the characteristics of different locations to obtain lower 2 Complexity dimensions, and it can prevent the problem of overfitting [22]. On this basis, a slight adjustment to the network framework can improve the generalization ability and robustness of the model [23].…”

Section: Introductionmentioning

confidence: 99%

A Marine Object Detection Algorithm Based on SSD and Feature Enhancement

Lu²,

Lu³

et al. 2020

Complexity

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Autonomous detection and fishing by underwater robots will be the main way to obtain aquatic products in the future; sea urchins are the main research object of aquatic product detection. When the classical Single-Shot MultiBox Detector (SSD) algorithm is applied to the detection of sea urchins, it also has disadvantages of being inaccurate to small targets and insensitive to the direction of the sea urchin. Based on the classic SSD algorithm, this paper proposes a feature-enhanced sea urchin detection algorithm. Firstly, according to the spiny-edge characteristics of a sea urchin, a multidirectional edge detection algorithm is proposed to enhance the feature, which is taken as the 4th channel of image and the original 3 channels of underwater image together as the input for the further deep learning. Then, in order to improve the shortcomings of SSD algorithm’s poor ability to detect small targets, resnet 50 is used as the basic framework of the network, and the idea of feature cross-level fusion is adopted to improve the feature expression ability and strengthen semantic information. The open data set provided by the National Natural Science Foundation of China underwater Robot Competition will be used as the test set and training set. Under the same training and test conditions, the AP value of the algorithm in this paper reaches 81.0%, 7.6% higher than the classic SSD algorithm, and the confidence of small target analysis is also improved. Experimental results show that the algorithm in this paper can effectively improve the accuracy of sea urchin detection.

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Dilated residual attention network for load disaggregation

Cited by 41 publications

References 39 publications

Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

Feature Guided CNN for Baby’s Facial Expression Recognition

A Marine Object Detection Algorithm Based on SSD and Feature Enhancement

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