scholarly journals The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4

Agronomy ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Chengliang Zhang ◽  
Tianhui Li ◽  
Wenbin Zhang
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Recognition Rate ◽  
Impurity Content ◽  
Economic Benefits ◽  
Detection Accuracy ◽  
Seed Cotton ◽  
Impurity Removal ◽  
Removal Equipment ◽  
Impurity Detection

The detection of cotton impurity rates can reflect the cleaning effect of cotton impurity removal equipment, which plays a vital role in improving cotton quality and economic benefits. Therefore, several studies are being carried out to improve detection accuracy. Image processing technology is increasingly used in cotton impurity detection, in which deep learning technology based on convolution neural networks has shown excellent results in image classification, segmentation, target detection, etc. However, most of these applications focus on detecting foreign fibers in lint, which is of little significance to the parameter adjustment of cotton impurity removal equipment. For this reason, our goal was to develop an impurity detection system for seed cotton. In image segmentation, we propose a multi-channel fusion segmentation algorithm to segment the machine-picked seed cotton image. We collected 1017 images of machine-picked seed cotton as a dataset to train the detection model and tested and recognized 100 groups of samples, with an average recognition rate of 94.1%. Finally, the image segmented by the multi-channel fusion algorithm is input into the improved YOLOv4 network model for classification and recognition, and the established V–W model calculates the content of all kinds of impurities. The experimental results show that the impurity content in machine-picked cotton can be obtained effectively, and the detection accuracy of the impurity rate can increase by 5.6%.

scholarly journals Quality Detection System of Transparent Nonel Tubes Based on Image Processing

2015 ◽  
Vol 9 (1) ◽  
pp. 697-702
Author(s):  
Guodong Sun ◽  
Wei Xu ◽  
Lei Peng
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Detection System ◽  
Recognition Rate ◽  
Human Vision ◽  
Detection Methods ◽  
Detection Accuracy ◽  
External Diameter ◽  
Quality Detection

The traditional quality detection method for transparent Nonel tubes relies on human vision, which is inefficient and susceptible to subjective factors. Especially for Nonel tubes filled with the explosive, missed defects would lead to potential danger in blasting engineering. The factors affecting the quality of Nonel tubes mainly include the uniformity of explosive filling and the external diameter of Nonel tubes. The existing detection methods, such as Scalar method, Analysis method and infrared detection technology, suffer from the following drawbacks: low detection accuracy, low efficiency and limited detection items. A new quality detection system of Nonel tubes has been developed based on machine vision in order to overcome these drawbacks. Firstly the system architecture for quality detection is presented. Then the detection method of explosive dosage and the relevant criteria are proposed based on mapping relationship between the explosive dosage and the gray value in order to detect the excessive explosive faults, insufficient explosive faults and black spots. Finally an algorithm based on image processing is designed to measure the external diameter of Nonel tubes. The experiments and practical operations in several Nonel tube manufacturers have proved the defect recognition rate of proposed system can surpass 95% at the detection speed of 100m/min, and system performance can meet the quality detection requirements of Nonel tubes. Therefore this quality detection method can save human resources and ensure the quality of Nonel tubes.

A Detection System of Impurity in Transparent Liquid

2014 ◽  
Vol 1003 ◽  
pp. 193-197 ◽  
Author(s):  
Bin Huang ◽  
Ping Wang ◽  
Si Le Ma
Keyword(s):  
Error Detection ◽  
Detection Method ◽  
Detection System ◽  
Recognition Rate ◽  
Difficult Problem ◽  
Flow Diagram ◽  
Detection Accuracy ◽  
Liquid Impurity ◽  
Transparent Liquid ◽  
Impurity Detection

In the fields of transparent liquid impurity detection based on machine vision technology, how to effectively detect impurities in the liquid is a difficult problem which has not yet been solved, mainly in the low recognition rate, the slow recognition speed, and the phenomenon of error detection and undetected. Therefore, this paper presents a new impurity detection method. Firstly, the hardware structure of the system is introduced in this paper. Then the flow diagram of impurity detection is presented. Finally, the algorithm of impurity detection is studied. Experiments show that the system introduced in this paper can identify impurities in liquid well on condition of ensuring the detection speed and detection accuracy.

A Detection System of Tool Condition Based on Image Processing Technology

2012 ◽  
Vol 510 ◽  
pp. 375-379
Author(s):  
Xing Yuan Kou ◽  
Chen Sheng Wang ◽  
Lei Li
Keyword(s):  
Image Processing ◽  
High Speed ◽  
Detection System ◽  
Digital Processing ◽  
Processing Technology ◽  
Detection Accuracy ◽  
Size Change ◽  
Tool Condition ◽  
Image Processing Technology ◽  
Processing Solution

This paper focused on introducing a real-time detection system of tool condition based on image processing technology and determined final algorithm after analyzing several different operators of edge detection. Firstly, the system will complete an acquisition of tool image, and transfer it to the computer. Then the image will be processed by some digital processing algorithms. At last, we can get the tool wear according to the size change of a tool. The result of this study showed that the detection system and its image processing solution could give a desired result. The detection accuracy could reach macron level, and the degree of automation could be improved too. So the system can be used to better meet the needs of high precision and high-speed in modern production.

scholarly journals Implementation of Pavement Defect Detection System on Edge Computing Platform

2021 ◽  
Vol 11 (8) ◽  
pp. 3725
Author(s):  
Yu-Chen Lin ◽  
Wen-Hui Chen ◽  
Cheng-Hsuan Kuo
Keyword(s):  
Embedded System ◽  
Defect Detection ◽  
Detection System ◽  
Recognition Rate ◽  
Developed Countries ◽  
Edge Computing ◽  
Filter Method ◽  
Detection Accuracy ◽  
Computing Platform ◽  
Detection Strategy

Road surfaces in Taiwan, as well as other developed countries, often experience structural failures, such as patches, bumps, longitudinal and lateral cracking, and potholes, which cause discomfort and pose direct safety risks to motorists. To minimize damage to vehicles from pavement defects or provide the corresponding comfortable ride promotion strategy later, in this study, we developed a pavement defect detection system using a deep learning perception scheme for implementation on Xilinx Edge AI platforms. To increase the detection distance and accuracy of pavement defects, two cameras with different fields of view, at 70∘ and 30∘, respectively, were used to capture the front views of a car, and then the YOLOv3 (you only look once, version 3) model was employed to recognize the pavement defects, such as potholes, cracks, manhole covers, patches, and bumps. In addition, to promote continuous pavement defect recognition rate, a tracking-via-detection strategy was employed, which first detects pavement defects in each frame and then associates them to different frames using the Kalman filter method. Thus, the average detection accuracy of the pothole category could reach 71%, and the miss rate was about 29%. To confirm the effectiveness of the proposed detection strategy, experiments were conducted on an established Taiwan pavement defect image dataset (TPDID), which is the first dataset for Taiwan pavement defects. Moreover, different AI methods were used to detect the pavement defects for quantitative comparative analysis. Finally, a field-programmable gate-array-based edge computing platform was used as an embedded system to implement the proposed YOLOv3-based pavement defect detection system; the execution speed reached 27.8 FPS while maintaining the accuracy of the original system model.

scholarly journals A flour impurity detection system based on image processing

2020 ◽  
Vol 1634 ◽  
pp. 012129
Author(s):  
Jimin Zhao ◽  
Chenchen Xue ◽  
Chenchen Xue
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Impurity Detection

Development of Detection System for Cotton Plastic Covering Using Ultrasonic

2013 ◽  
Vol 411-414 ◽  
pp. 1439-1444 ◽  
Author(s):  
Qi Zhang ◽  
Liang Hua ◽  
Juan Yao
Keyword(s):  
High Resolution ◽  
Detection System ◽  
Experimental Result ◽  
Detection Accuracy ◽  
Plastic Film ◽  
Cotton Production ◽  
Seed Cotton ◽  
Echo Signal ◽  
Great Loss ◽  
High Detection

Polyethylene is widely used as mulching plastic film in china cotton area. Collecting plastic film residue using machine is difficult. The cotton mixed with plastic film residue is badly harmful to cotton production, and causes great loss to cotton spinning enterprise. The detection system for cotton plastic covering using ultrasonic is developed in the paper. Sound wave echo method is used to detect plastic film residue mixed in seed cotton. The SCM is used to process echo signal and compensate measurement error caused by temperature variation. Experimental result shows that the system developed in the paper have features of low lost, high detection accuracy, high resolution and it has broad prospect in application.

Traffic incident detection system based on video image processing

2008 ◽  
Vol 28 (7) ◽  
pp. 1886-1889 ◽  
Author(s):  
Qin WANG ◽  
Shan HUANG ◽  
Hong-bin ZHANG ◽  
Quan YANG ◽  
Jian-jun ZHANG
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Video Image ◽  
Incident Detection ◽  
Traffic Incident ◽  
Video Image Processing

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Positive Impact ◽  
Detection System ◽  
Training Data ◽  
Detection Accuracy ◽  
Current State ◽  
Alignment Task ◽  
Scanned Image ◽  
Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence

2021 ◽  
pp. 1748006X2199538
Author(s):  
Zhenying Xu ◽  
Ziqian Wu ◽  
Wei Fan
Keyword(s):  
Defect Detection ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Detection Methods ◽  
Small Scale ◽  
Detection Accuracy ◽  
Single Shot ◽  
Surface Defect Detection ◽  
Feature Pyramid ◽  
Small Feature

Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.

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