scholarly journals Pedestrian Detection Algorithm Based on Improved Convolutional Neural Network

2017 ◽  
Vol 21 (5) ◽  
pp. 834-839 ◽  
Author(s):  
Qin Qin ◽  
Josef Vychodil ◽  
◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Detection ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
The Neural Network ◽  
Detection Ratio ◽  
Detection Window ◽  
Better Than

This paper proposes a new multi-feature detection method of local pedestrian based on a convolutional neural network (CNN), which provides a reliable basis for multi-feature fusion in pedestrian detection. According to the standard of pedestrian detection ratio, the pedestrian under the detection window would be segmented, using the sample labels to guide the local characteristics of CNN learning, the supervised learning after the network can obtain the local feature fusion more pedestrian description ability. Finally, a large number of experiments have been performed. The experimental results show that the local features of the neural network are better than those of most pedestrian features and combination features.

scholarly journals Pedestrian detection based on improved LeNet-5 convolutional neural network

2019 ◽  
Vol 13 ◽  
pp. 174830261987360 ◽  
Author(s):  
Chuan-Wei Zhang ◽  
Meng-Yue Yang ◽  
Hong-Jun Zeng ◽  
Jian-Ping Wen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Network Model ◽  
Pedestrian Detection ◽  
The Real ◽  
Better Than

In this article, according to the real-time and accuracy requirements of advanced vehicle-assisted driving in pedestrian detection, an improved LeNet-5 convolutional neural network is proposed. Firstly, the structure of LeNet-5 network model is analyzed, and the structure and parameters of the network are improved and optimized on the basis of this network to get a new LeNet network model, and then it is used to detect pedestrians. Finally, the miss rate of the improved LeNet convolutional neural network is found to be 25% by contrast and analysis. The experiment proves that this method is better than SA-Fast R-CNN and classical LeNet-5 CNN algorithm.

Research on High-Density Network Intrusion Features Detection Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 1054-1057
Author(s):  
Tai Fu Lv
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Systems ◽  
Feature Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
High Density ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
The Neural Network

Research on high-density network intrusion features problems, which improves the detection accuracy. For high-density network, an intrusion feature detection system based on intelligent expert systems and neural networks in is proposed. First, use expert systems for known high-density network intrusion detection. Use the neural network expert system to detect those which cannot find the unknown high-density network intrusion. Finally test results using neural network expert system rule library to be updated. Experimental results show that this method can effectively detect high-density network intrusion features, which ensures the security of the network and achieves satisfactory results.

Fabric defect detection based on feature fusion of a convolutional neural network and optimized extreme learning machine

2021 ◽  
pp. 004051752110447
Author(s):  
Zhiyu Zhou ◽  
Wenxiong Deng ◽  
Zefei Zhu ◽  
Yaming Wang ◽  
Jiayou Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Extreme Learning Machine ◽  
Defect Detection ◽  
Feature Fusion ◽  
Principal Component ◽  
Detection Algorithm ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Learning Machine

Aiming to accurately detect various defects in the fabric production process, we propose a fabric defect detection algorithm based on the feature fusion of a convolutional neural network (CNN) and optimized extreme learning machine (ELM). Firstly, we use transfer learning to transfer the parameters of the first 13 convolutional layers and first two fully connected layers of a VGG16 network model as pre-trained by ImageNet to the initial model and fine-tune the parameters. Subsequently, the fine-tuned model is used as a feature extractor to extract features of RGB images and their corresponding L-component images. A principal component analysis is used to reduce the dimensionality of the features and fuse the reduced features. The moth flame optimization (MFO) algorithm is used to initialize the optimization variables of a parallel chaotic search (PCS) algorithm, and the PCS algorithm (as optimized by the MFO algorithm) is used to optimize the input weight and bias of the ELM (i.e., the PCS-MFO-ELM (PMELM)). Finally, the PMELM is used to replace the softmax classifier of the CNN to classify and detect fabric defect features. The experimental results show that on the amplified TILDA dataset, the precision, recall, F1-score, and accuracy rates of this algorithm for fabric holes, stains, warp breaks, dragging, and folds in fabric can reach 98.57%, 98.52%, 98.52%, and 98.50%, respectively, that is, higher than those of other algorithms. Through a validity experiment, this method is shown to be suitable for defect detection for unpatterned fabrics, regular patterned fabrics, and irregularly patterned fabrics.

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