scholarly journals Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Haitao Chen
Keyword(s):  
Feature Extraction ◽  
Solid Waste ◽  
Target Detection ◽  
Image Recognition ◽  
Detection System ◽  
Real Life ◽  
Detection Algorithm ◽  
Neural Nets ◽  
Recycling System ◽  
Softmax Classifier

In this paper, the technique of image recognition algorithm is used to conduct an in-depth study and analysis of the intelligent classification and recycling system of solid waste and to optimize the design of its system. The network structure and detection principle of the YOLO target detection algorithm based on convolutional neural nets are analysed, images of construction solid waste are collected as a dataset, and the image dataset is expanded using data enhancement techniques, and the target objects in the dataset are labelled and used to train their own YOLO detection models. To facilitate testing the images and to design a YOLO algorithm-based construction solid waste target detection system. Using the detection system for construction solid waste recognition, the YOLO model can accurately detect the location, class, and confidential information of the target object in the image. Image recognition is a technique to recognize images by capturing real-life images through devices and performing feature extraction, and this technique has been widely used since its inception. The deep learning-based classification algorithm for recyclable solid waste studied in this paper can classify solid waste efficiently and accurately, solving the problem that people do not know how to classify solid waste in daily life. The convolutional layer, pooling layer, and fully connected layer in a convolutional neural network are responsible for feature extraction, reducing the number of parameters, integrating features into high-level features, and finally classifying them by SoftMax classifier in turn. However, the actual situation is intricate and often the result is not obtained as envisioned, and the use of migration learning can be a good way to improve the overfitting phenomenon. In this paper, the combination of lazy optimizer and lookahead can improve the generalization ability and fitting speed as well as greatly improve the accuracy and stability. The experimental results are tested, and it is found that the solid waste classification accuracy can be as high as 95% when the VGG19 model is selected and the optimizer is combined.

Intrusion detection algorithm based on image enhanced convolutional neural network

2021 ◽  
pp. 1-12
Author(s):  
Qian Wang ◽  
Wenfang Zhao ◽  
Jiadong Ren
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Binary Classification ◽  
Original Data ◽  
Detection Algorithm ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Softmax Classifier

Intrusion Detection System (IDS) can reduce the losses caused by intrusion behaviors and protect users’ information security. The effectiveness of IDS depends on the performance of the algorithm used in identifying intrusions. And traditional machine learning algorithms are limited to deal with the intrusion data with the characteristics of high-dimensionality, nonlinearity and imbalance. Therefore, this paper proposes an Intrusion Detection algorithm based on Image Enhanced Convolutional Neural Network (ID-IE-CNN). Firstly, based on the image processing technology of deep learning, oversampling method is used to increase the amount of original data to achieve data balance. Secondly, the one-dimensional data is converted into two-dimensional image data, the convolutional layer and the pooling layer are used to extract the main features of the image to reduce the data dimensionality. Third, the Tanh function is introduced as an activation function to fit nonlinear data, a fully connected layer is used to integrate local information, and the generalization ability of the prediction model is improved by the Dropout method. Finally, the Softmax classifier is used to predict the behavior of intrusion detection. This paper uses the KDDCup99 data set and compares with other competitive algorithms. Both in the performance of binary classification and multi-classification, ID-IE-CNN is better than the compared algorithms, which verifies its superiority.

scholarly journals Slope Collapse Detection Based on Image Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin-Dai An ◽  
Xiang-Wen Xie ◽  
Di Wu ◽  
Ke-Feng Song
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Recognition ◽  
Motion Detection ◽  
Detection Algorithm ◽  
Image Feature ◽  
Moving Targets ◽  
Image Feature Extraction ◽  
Slope Collapse ◽  
River Embankment

In this paper, we study a task of slope collapse detection (SCD) for river embankment and formulate it as the tasks of motion detection and image recognition. Specifically, we introduce an SCD method based on motion detection and image recognition technologies to help inspector attendants detect the slope collapse. In this method, we use the foreground motion detection algorithm to identify the slope collapse of the scene of the river embankment. Since the moving targets in the foreground may not only be the slope collapse but also maybe some biology, we further use the image feature extraction and image recognition technology to recognize the foreground motion area, thus eliminating the influence of the biology on the detection results. Experimental results on the relevant scene data show that the proposed method can identify the slope collapse in real-time, and can effectively eliminate the motion interference of the biology, which has a high practical value.

scholarly journals HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway

2020 ◽  
Vol 14 (3) ◽  
pp. 231
Author(s):  
Firnanda Al Islama Achyunda Putra ◽  
Fitri Utaminingrum ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Feature Extraction ◽  
Early Warning System ◽  
Detection System ◽  
Warning System ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Knn Classifier ◽  
Knn Classification ◽  
Testing Data ◽  
Main Components

Autonomous car is a vehicle that can guide itself without human intervention. Various types of rudderless vehicles are being developed. Future systems where computers take over the art of driving. The problem is prior to being attention in an autonomous car for obtaining the high safety. Autonomous car need early warning system to avoid accidents in front of the car, especially the system can be used in the Highway location. In this paper, we propose a vision-based vehicle detection system for Autonomous car. Our detection algorithm consists of three main components: HOG feature extraction, KNN classifier, and vehicle detection. Feature extraction has been used to recognize an object such as cars. In this case, we use HOG feature extraction to detect as a car or non-car. We use the KNN algorithm to classify. KNN Classification in previous studies had quite good results. Car detected by matching about trining data with testing data. Trining data created by extract HOG feature from image 304 x 240 pixels. The system will produce a classification between car or non-car.

scholarly journals FPGA-Based Real-Time Moving Target Detection System for Unmanned Aerial Vehicle Application

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Jia Wei Tang ◽  
Nasir Shaikh-Husin ◽  
Usman Ullah Sheikh ◽  
M. N. Marsono
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Moving Target ◽  
Moving Target Detection ◽  
Rescue Operation ◽  
Aerial Vehicle

Moving target detection is the most common task for Unmanned Aerial Vehicle (UAV) to find and track object of interest from a bird’s eye view in mobile aerial surveillance for civilian applications such as search and rescue operation. The complex detection algorithm can be implemented in a real-time embedded system using Field Programmable Gate Array (FPGA). This paper presents the development of real-time moving target detection System-on-Chip (SoC) using FPGA for deployment on a UAV. The detection algorithm utilizes area-based image registration technique which includes motion estimation and object segmentation processes. The moving target detection system has been prototyped on a low-cost Terasic DE2-115 board mounted with TRDB-D5M camera. The system consists of Nios II processor and stream-oriented dedicated hardware accelerators running at 100 MHz clock rate, achieving 30-frame per second processing speed for 640 × 480 pixels’ resolution greyscale videos.

scholarly journals Detection of weak multi-target with adjacent frequency based on chaotic system

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771989024
Author(s):  
Dawei Chen ◽  
Shuo Shi ◽  
Xuemai Gu ◽  
Byonghyo Shim ◽  
Qianyao Ren
Keyword(s):  
Signal Detection ◽  
Target Detection ◽  
Narrow Band ◽  
Detection System ◽  
Additive White Gaussian Noise ◽  
Melnikov Method ◽  
Detection Algorithm ◽  
Weak Signal Detection ◽  
Narrow Band Noise ◽  
Band Noise

As a promising technology in signal detection, the chaotic detection system can significantly improve the accuracy of weak signal detection in strong background noise. It benefits from its characteristics of the sensitivity to the initial condition and the immunity to the Additive White Gaussian Noise. However, the fundamental challenges of the existing chaotic detection system are the sensitivity to narrow-band noise and the influences of multi-target detection with adjacent frequency, which bring great difficulties in the real application. To address these problems, in this article, we focus on the weak multi-target detection with adjacent frequency under the narrow-band noise, and a novel chaotic detection system that integrates the detection algorithm based on period-chaos duration ratio is proposed. In order to enhance the robustness to narrow-band noise, the Melnikov method is used to analyze the Duffing difference system. To realize the detection of weak multi-target with adjacent frequency, we proposed the detection system using the rule named general critical state. Furthermore, simulation results corroborate that the proposed system based on period-chaos duration ratio can achieve satisfactory performance in terms of the weak multi-target detection under narrow-band noise, and it is well investigated by extensive simulation for testing its effectiveness.

scholarly journals Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 59
Author(s):  
Heqing Huang ◽  
Tongbin Huang ◽  
Zhen Li ◽  
Shilei Lyu ◽  
Tao Hong
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Detection System ◽  
Citrus Fruit ◽  
Detection Algorithm ◽  
Edge Computing ◽  
Detection Accuracy ◽  
Computing Device ◽  
Adaptive Fusion ◽  
Aerial Vehicle

Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus detection task that combines UAV data collection, AI embedded device, and target detection algorithm. The system used a small unmanned aerial vehicle equipped with a camera to take full-scale pictures of citrus trees; at the same time, we extended the state-of-the-art model target detection algorithm, added the attention mechanism and adaptive fusion feature method, improved the model’s performance; to facilitate the deployment of the model, we used the pruning method to reduce the amount of model calculation and parameters. The improved target detection algorithm is ported to the edge computing end to detect the data collected by the unmanned aerial vehicle. The experiment was performed on the self-made citrus dataset, the detection accuracy was 93.32%, and the processing speed at the edge computing device was 180 ms/frame. This method is suitable for citrus detection tasks in the mountainous orchard environment, and it can help fruit growers to estimate their yield.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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