scholarly journals Detection of Key Organs in Tomato Based on Deep Migration Learning in a Complex Background

Agriculture ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 196 ◽  
Author(s):  
Jun Sun ◽  
Xiaofei He ◽  
Xiao Ge ◽  
Xiaohong Wu ◽  
Jifeng Shen ◽  
...  
Keyword(s):  
Embedded System ◽  
Theoretical Foundation ◽  
Detection Method ◽  
Training Model ◽  
Detection Accuracy ◽  
Test Results ◽  
Detection Model ◽  
Automatic Picking ◽  
The Mean ◽  
The Embedded System

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomatoes and the plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rates and poor generalizations of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network (CNN) has been proposed in this paper. Based on the original Faster R-CNN algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature extraction network, and a K-means clustering method was used to adjust more appropriate anchor sizes than manual setting, to improve detection accuracy. The test results showed that the mean average precision (mAP) was significantly improved compared with the traditional Faster R-CNN model. The training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of a precise targeting pesticide application system and an automatic picking device.

scholarly journals Detection of Key Organs in Tomato Based on Deep Migration Learning in Complex Background

2018 ◽  
Author(s):  
Jun Sun ◽  
Xiaofei He ◽  
Xiao Ge ◽  
Xiaohong Wu ◽  
Jifeng Shen ◽  
...  
Keyword(s):  
Embedded System ◽  
Data Augmentation ◽  
Recognition Rate ◽  
Training Model ◽  
Detection Accuracy ◽  
Test Results ◽  
Detection Model ◽  
Automatic Picking ◽  
Augmentation Techniques ◽  
The Embedded System

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomato and plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rate and poor generalization of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network has been proposed in this paper. Based on the original Faster R-CNN algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature extraction network, and K-means clustering method was used to adjust more appropriate anchor size than manual setting to improve detection accuracy. A variety of data augmentation techniques were used to train the network. The test results showed that compared with the traditional Faster R-CNN model, the mean average precision (mAP) of the optimal model was improved from 85.2% to 90.7%, the memory requirement decreased from 546.9MB to 115.9 MB, and the average detection time was shortened to 0.073S/sheet. As the performance greatly improved, the training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of precise targeting pesticide application system and automatic picking device.

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

A real time detection method for abnormal strapping of steel coil based on CCD active imaging

2021 ◽  
Author(s):  
Chuan Ye ◽  
Liming Zhao ◽  
Qiyan Wang ◽  
Bo Pan ◽  
Youchun Xie ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Detection Method ◽  
Saliency Detection ◽  
Automatic Segmentation ◽  
Engineering Application ◽  
Detection Accuracy ◽  
Steel Coil ◽  
Detection Model ◽  
Laser Stripe ◽  
Active Imaging

Abstract In order to accurately detect the abnormal looseness of strapping in the process of steel coil hoisting, an accurate detection method of strapping abnormality based on CCD structured light active imaging is proposed. Firstly, a maximum entropy laser stripe automatic segmentation model integrating multi-scale saliency features is constructed. With the help of saliency detection model, the purpose is to reduce the interference of the environment to the laser stripe and highlight the distinguishability between the stripe and the background. Then, the maximum entropy is used to segment the fused saliency features and accurately extract the stripe contour. Finally, the stripe normal field is obtained by calculating the stripe gradient vector, the stripe center line is extracted based on the stripe distribution normal direction, and the abnormal strapping is recognized online according to the stripe center. Experiments show that the proposed method is effective in terms of detection accuracy and time efficiency, and has certain engineering application value.

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

scholarly journals A Robust Forgery Detection Method for Copy–Move and Splicing Attacks in Images

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1500
Author(s):  
Mohammad Manzurul Islam ◽  
Gour Karmakar ◽  
Joarder Kamruzzaman ◽  
Manzur Murshed
Keyword(s):  
Model Building ◽  
Performance Metrics ◽  
Detection Method ◽  
Color Image ◽  
Computational Time ◽  
Support Vector ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Image Forgery ◽  
The Mean

Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy–move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors’ physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.

Intelligent Vision Tester Based on Embedded System

2014 ◽  
Vol 971-973 ◽  
pp. 1234-1237
Author(s):  
Xue Feng Yang ◽  
Li Gang Chen
Keyword(s):  
Visual Acuity ◽  
Remote Control ◽  
Embedded System ◽  
Digital Display ◽  
Test Results ◽  
Testing Instrument ◽  
Power Supply Circuit ◽  
Measuring Efficiency ◽  
Stable Performance ◽  
The Embedded System

To solve the problem that artificial visual acuity measuring efficiency is low in hospital and school,and waste of resources,this design introduces an intelligent vision test instrument.The instrument is controlled by Embedded system [1,2], including the power supply circuit, a wireless remote control circuit module,LED display circuit,digital display circuit,a voice circuit etc.Beginning of vision test after audio prompt.This time the LED random light under every line in the standard visual acuity chart.The test selects the direction of wireless remote control rocker of "up", "down", "left", "right"through the "E" the opening direction on standard visual acuity chart.Data is transmitted to the Embedded system,after data processing,to judge whether he is right and wrong,and then displayed.Intelligent vision testing instrument can achieve the anticipated goal,finally complete the design and production, The overall structure of the circuit device is simple, stable performance, the test results meet the requirements.

Ultrasonic Diagnosis of Breast Nodules Using Modified Faster R-CNN

2019 ◽  
Vol 41 (6) ◽  
pp. 353-367 ◽  
Author(s):  
Zihao Zhang ◽  
Xuesheng Zhang ◽  
Xiaona Lin ◽  
Licong Dong ◽  
Sure Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Detection Method ◽  
Malignant Tumors ◽  
Ultrasonic Diagnosis ◽  
Test Results ◽  
Average Precision ◽  
Multi Scale ◽  
The Mean

Breast cancer has become the biggest threat to female health. Ultrasonic diagnosis of breast cancer based on artificial intelligence is basically a classification of benign and malignant tumors, which does not meet clinical demand. Besides, the current target detection method performs poorly in detecting small lesions, while it is clinically required to detect nodules below 2 mm. The objective of this study is to (a) propose a diagnostic method based on Breast Imaging Reporting and Data System (BI-RADS) and (b) increase its detectability of small lesions. We modified the framework of Faster R-CNN (Faster Region-based Convolutional Neural Network) by introducing multi-scale feature extraction and multi-resolution candidate bound extraction into the network. Then, it was trained using 852 images of BI-RADS C2, 739 images of C3, and 1662 images of malignancy (BI-RADS 4a/4b/4c/5/6). We compared our model with unmodified Faster R-CNN and YOLO v3 (You Only Look Once v3). The mean average precision (mAP) is significantly increased to 0.913, while its average detection speed is slightly declined to 4.11 FPS (frames per second). Meanwhile, its detectivity of small lesions is effectively improved. Moreover, we also tentatively applied our model on video sequences and got satisfactory results. We modified Faster R-CNN and trained it partly based on BI-RADS. Its detectability of lesions, as well as small nodules, was significantly improved. In view of wide coverage of dataset and satisfactory test results, our method can basically meet clinical needs.

Eye State Detection Based on Embedded Linux System

2013 ◽  
Vol 457-458 ◽  
pp. 1253-1256
Author(s):  
Rui Li ◽  
Xin Wang ◽  
Jian Chun Jiang ◽  
Hong Yun Yang
Keyword(s):  
Image Processing ◽  
Embedded System ◽  
Detection Method ◽  
Video Stream ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Video Capture ◽  
State Detection ◽  
Embedded Linux System ◽  
The Embedded System

Eye state detection is dramatically influenced by the position of iris, for this reason, this paper proposed an eye state detection method combined the area between the eyelid with the eyelid contour. By modifying and transplanting V4L-utils and OpenCV image processing library, video capture and display software is built on the Cortex-A8 embedded system. Through experimental verification, the embedded system can realize the acquisition, processing and display of the video stream and the eye state detection algorithm also has high accuracy.

The Design of Embedded Ethernet System Based Microcontroller

2014 ◽  
Vol 1046 ◽  
pp. 352-355
Author(s):  
Song Lin Huang ◽  
Jian Zhong Cui
Keyword(s):  
Embedded System ◽  
Internet Technology ◽  
The Internet ◽  
Protocol Stack ◽  
Test Results ◽  
Embedded Devices ◽  
Collaborative Software ◽  
Software And Hardware ◽  
The Embedded System ◽  
Future Direction

With the wide application of Internet technology, the embedded system is becoming more and more to develop in the direction of the network. The combination of embedded devices and the Internet represents the future direction of development of embedded systems. In this paper, a microprocessor-based embedded Ethernet solution was presented. The collaborative software and hardware design thought was used in system. TheμC /OS-II operating system, joined the TCP/IP protocol stack, was transplanted to the embedded Ethernet platform. The test results proved that the embedded Ethernet system network communication is stable and reliable.

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