Pedestrian detection algorithm based on multi-scale feature extraction and attention feature fusion

2021 ◽  
pp. 103311
Author(s):  
Hao Xia ◽  
Jun Ma ◽  
Jiayu Ou ◽  
Xinyao Lv ◽  
Chengjie Bai
Keyword(s):  
Feature Extraction ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Scale Feature ◽  
Multi Scale

scholarly journals Pedestrian detection algorithm based on improved muti-scale feature fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012008
Author(s):  
Hui Liu ◽  
Keyang Cheng
Keyword(s):  
Clustering Algorithm ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Data Sets ◽  
False Detection ◽  
Scale Feature ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Small Targets

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

scholarly journals Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 205 ◽  
Author(s):  
Chan Zeng ◽  
Junfeng Zheng ◽  
Jiangyun Li
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Load Distribution ◽  
Feature Fusion ◽  
Conveyor Belt ◽  
Detection Algorithm ◽  
Scale Feature ◽  
Multi Scale ◽  
High Level ◽  
Detection Effect

The conveyor belt is an indispensable piece of conveying equipment for a mine whose deviation caused by roller sticky material and uneven load distribution is the most common failure during operation. In this paper, a real-time conveyor belt detection algorithm based on a multi-scale feature fusion network is proposed, which mainly includes two parts: the feature extraction module and the deviation detection module. The feature extraction module uses a multi-scale feature fusion network structure to fuse low-level features with rich position and detail information and high-level features with stronger semantic information to improve network detection performance. Depthwise separable convolutions are used to achieve real-time detection. The deviation detection module identifies and monitors the deviation fault by calculating the offset of conveyor belt. In particular, a new weighted loss function is designed to optimize the network and to improve the detection effect of the conveyor belt edge. In order to evaluate the effectiveness of the proposed method, the Canny algorithm, FCNs, UNet and Deeplab v3 networks are selected for comparison. The experimental results show that the proposed algorithm achieves 78.92% in terms of pixel accuracy (PA), and reaches 13.4 FPS (Frames per Second) with the error of less than 3.2 mm, which outperforms the other four algorithms.

scholarly journals A Novel 2D-3D CNN with Spectral-Spatial Multi-Scale Feature Fusion for Hyperspectral Image Classification

2021 ◽  
Vol 13 (22) ◽  
pp. 4621
Author(s):  
Dongxu Liu ◽  
Guangliang Han ◽  
Peixun Liu ◽  
Hang Yang ◽  
Xinglong Sun ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Classification Scheme ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Spectral Feature ◽  
Hyperspectral Image Classification ◽  
Scale Feature ◽  
Multi Scale ◽  
3D Cnn

Multifarious hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have been gradually proposed and achieve a promising classification performance. However, hyperspectral image classification still suffers from various challenges, including abundant redundant information, insufficient spectral-spatial representation, irregular class distribution, and so forth. To address these issues, we propose a novel 2D-3D CNN with spectral-spatial multi-scale feature fusion for hyperspectral image classification, which consists of two feature extraction streams, a feature fusion module as well as a classification scheme. First, we employ two diverse backbone modules for feature representation, that is, the spectral feature and the spatial feature extraction streams. The former utilizes a hierarchical feature extraction module to capture multi-scale spectral features, while the latter extracts multi-stage spatial features by introducing a multi-level fusion structure. With these network units, the category attribute information of HSI can be fully excavated. Then, to output more complete and robust information for classification, a multi-scale spectral-spatial-semantic feature fusion module is presented based on a Decomposition-Reconstruction structure. Last of all, we innovate a classification scheme to lift the classification accuracy. Experimental results on three public datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

scholarly journals A new end-to-end image dehazing algorithm based on residual attention mechanism

2021 ◽  
Vol 39 (4) ◽  
pp. 901-908
Author(s):  
Zhenjian Yang ◽  
Jiamei Shang ◽  
Zhongwei Zhang ◽  
Yan Zhang ◽  
Shudong Liu
Keyword(s):  
Feature Extraction ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Scale Feature ◽  
Multi Scale ◽  
Color Distortion ◽  
End To End ◽  
Density Image

Traditional image dehazing algorithms based on prior knowledge and deep learning rely on the atmospheric scattering model and are easy to cause color distortion and incomplete dehazing. To solve these problems, an end-to-end image dehazing algorithm based on residual attention mechanism is proposed in this paper. The network includes four modules: encoder, multi-scale feature extraction, feature fusion and decoder. The encoder module encodes the input haze image into feature map, which is convenient for subsequent feature extraction and reduces memory consumption; the multi-scale feature extraction module includes residual smoothed dilated convolution module, residual block and efficient channel attention, which can expand the receptive field and extract different scale features by filtering and weighting; the feature fusion module with efficient channel attention adjusts the channel weight dynamically, acquires rich context information and suppresses redundant information so as to enhance the ability to extract haze density image of the network; finally, the encoder module maps the fused feature nonlinearly to obtain the haze density image and then restores the haze free image. The qualitative and quantitative tests based on SOTS test set and natural haze images show good objective and subjective evaluation results. This algorithm improves the problems of color distortion and incomplete dehazing effectively.

scholarly journals Adaptive Weighted Multi-Level Fusion of Multi-Scale Features: A New Approach to Pedestrian Detection

2021 ◽  
Vol 13 (2) ◽  
pp. 38
Author(s):  
Yao Xu ◽  
Qin Yu
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Feature Maps ◽  
Scale Feature ◽  
Multi Scale ◽  
One Stage ◽  
Current State ◽  
Multi Level ◽  
Feature Utilization

Great achievements have been made in pedestrian detection through deep learning. For detectors based on deep learning, making better use of features has become the key to their detection effect. While current pedestrian detectors have made efforts in feature utilization to improve their detection performance, the feature utilization is still inadequate. To solve the problem of inadequate feature utilization, we proposed the Multi-Level Feature Fusion Module (MFFM) and its Multi-Scale Feature Fusion Unit (MFFU) sub-module, which connect feature maps of the same scale and different scales by using horizontal and vertical connections and shortcut structures. All of these connections are accompanied by weights that can be learned; thus, they can be used as adaptive multi-level and multi-scale feature fusion modules to fuse the best features. Then, we built a complete pedestrian detector, the Adaptive Feature Fusion Detector (AFFDet), which is an anchor-free one-stage pedestrian detector that can make full use of features for detection. As a result, compared with other methods, our method has better performance on the challenging Caltech Pedestrian Detection Benchmark (Caltech) and has quite competitive speed. It is the current state-of-the-art one-stage pedestrian detection method.

scholarly journals Feature Extraction and Analysis of Microseismic Signal Based on Convolutional Neural Network

2021 ◽  
Vol 2143 (1) ◽  
pp. 012017
Author(s):  
Hui Zhang ◽  
Hao Zhai ◽  
Ke Zhang ◽  
Lujun Wang ◽  
Xing Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Big Data ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Practical Significance ◽  
Scale Feature ◽  
Multi Scale ◽  
Seismic Detection ◽  
Microseismic Signal

Abstract Seismic detection technology has been widely used in safety detection of engineering construction abroad. Although it has just started in the field of engineering in our country, its role is becoming more and more important. Through computer technology, micro-seismic detection can provide accurate data for the construction safety detection of large-scale projects, which has important practical significance for the rapid and effective identification of micro-seismic signals. Based on this, the purpose of this article is to study the feature extraction and classification of microseismic signals based on neural games. This article first summarizes the development status of microseismic monitoring technology. Using traditional convolutional neural networks for analysis, a multi-scale feature fusion network is proposed on the basis of convolutional neural networks and big data, the multi-scale feature fusion network is used to research and analyze microseismic feature extraction and classification. This article systematically explains The principle of microseismic signal acquisition and the construction of multi-scale feature fusion network. And use big data, comparative analysis method, observation method and other research methods to study the theme of this article. Experimental research shows that the db7 wavelet base has little effect on the Megatron signal.

scholarly journals Remote Sensing Imagery Super Resolution Based on Adaptive Multi-Scale Feature Fusion Network

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1142
Author(s):  
Xinying Wang ◽  
Yingdan Wu ◽  
Yang Ming ◽  
Hui Lv
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Feature Fusion ◽  
Super Resolution ◽  
Convolutional Network ◽  
Remote Sensing Imagery ◽  
Resolution Image ◽  
Scale Feature ◽  
Multi Scale ◽  
Key Characteristics

Due to increasingly complex factors of image degradation, inferring high-frequency details of remote sensing imagery is more difficult compared to ordinary digital photos. This paper proposes an adaptive multi-scale feature fusion network (AMFFN) for remote sensing image super-resolution. Firstly, the features are extracted from the original low-resolution image. Then several adaptive multi-scale feature extraction (AMFE) modules, the squeeze-and-excited and adaptive gating mechanisms are adopted for feature extraction and fusion. Finally, the sub-pixel convolution method is used to reconstruct the high-resolution image. Experiments are performed on three datasets, the key characteristics, such as the number of AMFEs and the gating connection way are studied, and super-resolution of remote sensing imagery of different scale factors are qualitatively and quantitatively analyzed. The results show that our method outperforms the classic methods, such as Super-Resolution Convolutional Neural Network(SRCNN), Efficient Sub-Pixel Convolutional Network (ESPCN), and multi-scale residual CNN(MSRN).

scholarly journals Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2420
Author(s):  
Pengfei Shi ◽  
Xiwang Xu ◽  
Jianjun Ni ◽  
Yuanxue Xin ◽  
Weisheng Huang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Fusion ◽  
Detection Algorithm ◽  
Training Data ◽  
Ecological Environment ◽  
Detection Accuracy ◽  
Biological Detection ◽  
Multi Scale ◽  
Feature Pyramid ◽  
Bounding Boxes

Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, such as the low-quality image, different sizes or shapes, and overlapping or occlusion of underwater organisms. Therefore, this paper proposes an underwater biological detection algorithm based on improved Faster-RCNN. Firstly, the ResNet is used as the backbone feature extraction network of Faster-RCNN. Then, BiFPN (Bidirectional Feature Pyramid Network) is used to build a ResNet–BiFPN structure which can improve the capability of feature extraction and multi-scale feature fusion. Additionally, EIoU (Effective IoU) is used to replace IoU to reduce the proportion of redundant bounding boxes in the training data. Moreover, K-means++ clustering is used to generate more suitable anchor boxes to improve detection accuracy. Finally, the experimental results show that the detection accuracy of underwater biological detection algorithm based on improved Faster-RCNN on URPC2018 dataset is improved to 88.94%, which is 8.26% higher than Faster-RCNN. The results fully prove the effectiveness of the proposed algorithm.

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