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Simple SummaryCorn pest recognition and detection is an important step for Integrated Pest Management. Generally, traditional methods adopt manual observation and counting in wild field to monitor the occurrence degree of corn pes...
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Simple SummaryCorn pest recognition and detection is an important step for Integrated Pest Management. Generally, traditional methods adopt manual observation and counting in wild field to monitor the occurrence degree of corn pests. However, this is time-consuming and labor-intensive. An accurate and automatic corn pest detection method based on a deep convolutional neural network has been proposed in this paper. Extensive experimental results on a large-scale corn pest dataset show that the proposed method has good performance and can achieve precise recognition and detection of corn pests.
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The enlargement of a digital image is a process of narrowing the sampling interval. Since the image before the enlargement and that after the enlargement have different Nyquist frequencies, it is necessary to predict or estimate T...
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The enlargement of a digital image is a process of narrowing the sampling interval. Since the image before the enlargement and that after the enlargement have different Nyquist frequencies, it is necessary to predict or estimate The high-frequency components that are lost in the image Before processing and to complement these components in The enlargement process. It is noted that the image can be Represented as the sum of Gaussian components and Laplacian components in a hierarchical structure (pyramid Representation).
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In this paper, we show a multilayed RBF network, such that the centers and widths will be found by supervised learning. Thus, we gain the structural efficiency of network so that the basis functions can be adaptively constructed t...
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In this paper, we show a multilayed RBF network, such that the centers and widths will be found by supervised learning. Thus, we gain the structural efficiency of network so that the basis functions can be adaptively constructed to approximate the input-output mapping using full use of all units and connections in the network, while the price to pay is to loose the advantage in global convergence. All the parameters are found by a globally convergent learning algorithm called magic bruch method. We also show a pyramid topology for a multilayed network which is expected to have higher efficiency in both estimation and generalization.
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As one of the most common diabetic complications, diabetic retinopathy (DR) can cause retinal damage, vision loss and even blindness. Automated DR grading technology has important clinical significance, which can help ophthalmolog...
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As one of the most common diabetic complications, diabetic retinopathy (DR) can cause retinal damage, vision loss and even blindness. Automated DR grading technology has important clinical significance, which can help ophthalmologists achieve rapid and early diagnosis. With the popularity of deep learning, DR grading based on the convolutional neural networks (CNNs) has become the mainstream method. Unfortunately, although the CNN-based method can achieve satisfactory diagnostic accuracy, it lacks significant clinical information. In this paper, a lesion-attention pyramid network (LAPN) is presented. The pyramid network integrates the subnetworks with different resolutions to get multi-scale features. In order to take the lesion regions in the high-resolution image as the diagnostic evidence, the low-resolution network calculates the lesion activation map (using the weakly-supervised localization method) and guides the high-resolution network to concentrate on the lesion regions. Furthermore, a lesion attention module (LAM) is designed to capture the complementary relationship between the high-resolution features and the low-resolution features, and to fuse the lesion activation map. Experiment results show that the proposed scheme outperforms other existing approaches, and the proposed method can provide lesion activation map with lesion consistency as an additional evidence for clinical diagnosis.
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Object detection could be recognized as an essential part of the research to scenarios such as automatic driving and pedestrian detection, etc. Among multiple types of target objects, the identification of small-scale objects face...
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Object detection could be recognized as an essential part of the research to scenarios such as automatic driving and pedestrian detection, etc. Among multiple types of target objects, the identification of small-scale objects faces significant challenges. We would introduce a new feature pyramid framework called Dual Attention based Feature Pyramid Network (DAFPN), which is designed to avoid predicament about multi-scale object recognition. In DAFPN, the attention mechanism is introduced by calculating the top-down pathway and lateral pathway, where the spatial attention, as well as channel attention, would participate, respectively, such that the pyramidal feature maps can be generated with enhanced spatial and channel interdependencies, which bring more semantical information for the feature pyramid. Using the COCO data set, which consists of a considerable quantity of small-scale objects, the experiments are implemented. The analysis results verify the optimized performance of DAFPN compared with the original Feature Pyramid Network (FPN) specifically for the identification on a small scale. The proposed DAFPN is promising for object detection in an era full of intelligent machines that need to detect multi-scale objects.
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A new method based on artificial neural networks for calculating the narrow aperture dimension of the pyramidal horn is presented. The Levenberg–Marquardt algorithm is used to train the networks. The narrow aperture dimension cal...
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A new method based on artificial neural networks for calculating the narrow aperture dimension of the pyramidal horn is presented. The Levenberg–Marquardt algorithm is used to train the networks. The narrow aperture dimension calculated using artificial neural networks is used in the optimum gain pyramidal horn design. The computed gains of the designed pyramidal horns are in very good agreement with the desired gains.
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Keeping record of daily meal intake is an effective solution for tackling with obesity and overweight. This can be done by developing apps on smartphones that are able to automatically recommend a short list of most probable foods...
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Keeping record of daily meal intake is an effective solution for tackling with obesity and overweight. This can be done by developing apps on smartphones that are able to automatically recommend a short list of most probable foods by analyzing the photo taken from food. Then, the user chooses the correct answer from the short list. Hence, the automatic food recognition system must be able to recommend an accurate list. In other words, it is not essential for these apps to have a very high top-1 accuracy. Considering that the app will show the list of 5 most probable foods, the food recognition system must have a high top-5 accuracy. A food recognition system is usually developed by adapting knowledge of state-of-the-art networks such as GoogleNet and ResNet to the domain of food. However, these networks have high number of parameters. In this paper, we propose a 23-layer architecture which has 99.14% and 96.63% fewer parameter compared with ResNet and GoogleNet. Our experiment on Food101 and UECFood-256 datasets shows that although our network reduces the number of parameters dramatically, it produces more accurate results than GoogleNet and its accuracy is comparable with ResNet. (c) 2017 Elsevier B.V. All rights reserved.
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Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224224) input image. This requirement is “artificial” and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale...
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Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224224) input image. This requirement is “artificial” and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this - ompetition.
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The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fie...
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The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.
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Abstract The research field of object detection has been a hotspot in computer vision. However, most of the one-stage lightweight object detection models based on the deep convolutional neural network have the problems of many par...
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Abstract The research field of object detection has been a hotspot in computer vision. However, most of the one-stage lightweight object detection models based on the deep convolutional neural network have the problems of many parameters. To address this problem, this paper proposes a new model named Fusion Shuffle Light Detector (FSLDet). First, based on the FSSD mode, we apply the improved lightweight Shufflenet V2 network to the FSSD model for feature extraction, where the improvement about ShuffleNet v2 is an adjustment for the network structure. Meanwhile, we adopt the bidirectional feature pyramid model to improve the feature fusion operation, which makes the fused features have more semantic information. Experiments were carried out on PASCAL VOC 2007 + 2012 dataset and helmet detection dataset. The experiment shows that the FSLDet model is superior to the state-of-the-art model in multiple evaluation criteria.
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