Evaluation of Detection Accuracy of Image Recognition for Automatic Counting of Rice Planthoppers Captured on Sticky Boards

Nowadays, along with the higher requirement of the customer and the standardization of enterprise management, the finished product of steel bar production must be standardization packaged by root number; management requirements of bar fixed bundle of sticks are more stringent. The artificial count is used into the most of the steel bar production recently. So there are many problems. Such as labor intensity, easy fatigue, less efficient, and error-prone. The image recognition technology for online automatic counting system for a given period of time. It also can improve the speed and make the product more accuracy. This paper mainly talks about the system composition and image processing algorithm.

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Research on Burning Zone Detection Method Based on Flame Image Recognition for Ceramic Roller Kiln

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.1761 ◽

2014 ◽

Vol 602-605 ◽

pp. 1761-1767

Author(s):

Yong Hong Zhu ◽

Peng Li

Keyword(s):

Image Recognition ◽

Working Conditions ◽

Detection Method ◽

Firing Process ◽

Detection Accuracy ◽

Process Data ◽

Fusion Algorithm ◽

Roller Kiln ◽

Burning Zone ◽

Ceramic Roller

In the firing process of ceramic products, the sintering conditions vary from firing phase to firing phase. In different firing phases, flame texture changes obviously, so it can be used as a important parameter of burning zone identification for ceramic roller kiln. In this paper, both flame image recognition of simulating artificial-look-fire and multi-point temperature detection technology are used to detect burning zone working conditions of ceramic roller kiln so as to greatly improve detection accuracy. The key data fusion algorithm of PTCR-based point detection temperature and flame image recognition–based detection method of burning zone working condition for ceramic roller kiln are proposed. The temperature measurement experiment system scheme of ceramic roller kiln burning zone is also given. The system can fuse the key process data with flame image characteristics so as to get the comprehensive database used to judge burning zone working conditions and temperatures. In the end, The testing experiment was carried out. The experimental results show that the method proposed above is feasible and effective.

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Research on Bolt Crack Detection Method Based on Image Processing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.426 ◽

2013 ◽

Vol 433-435 ◽

pp. 426-429

Author(s):

Jin Qiu Liu ◽

Bing Fa Zhang ◽

Yu Zeng Wang ◽

Guang Ya Li ◽

Jing Ru Han

Keyword(s):

Image Processing ◽

Edge Detection ◽

Image Recognition ◽

Crack Detection ◽

Detection Method ◽

Image Acquisition ◽

Detection Methods ◽

Detection Accuracy ◽

Contact Detection ◽

Fracture Identification

A method of non-contact detection of bolt fracture have serial steps as follows: First of all the required data is obtained through image acquisition, then through the edge detection, image recognition and other image processing on the image to get the bolt fracture identification results, finally the non-contact measurement bolt fracture is realized. Experiments show that bolt crack detection method based on image processing, compared with the traditional detection methods improve the efficiency of detection and improve the detection accuracy. The method for bolt crack detection is feasible.

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Research of Glass Fiber Textile Monitor Image Recognition Based on Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.1978 ◽

2013 ◽

Vol 694-697 ◽

pp. 1978-1982

Author(s):

Shu Qian Chen ◽

Yang Lie Fu

Keyword(s):

Neural Network ◽

Glass Fiber ◽

Image Recognition ◽

Detection Methods ◽

Detection Accuracy ◽

Textile Machine ◽

Weft Fiber ◽

The Stability ◽

Monitor Image ◽

Neural Network Input

Researched on weft fiber cut problems of glass fiber, improved the efficiency of textile production. Glass fiber textile machine is a major producer machine of glass fiber cloth. Textile machines weft detection usually uses the contact type in production, requires that the weft maintains certain pressure to the sensor. Using this method will cause glass fiber weft bristling, and will produce glass fiber floating dust. Damage to the textile machine and has the harm to the human body health. Used video surveillance method to detection the weft, image recognition and speed directly affects the stability of the system. This paper presented a detection methods of glass fiber textiles weft fiber cut based on neural network-based, selected multiple features which were directly related to the image with the weft as neural network input vector, through repeated training samples to remove tiny ripple effects which were caused by weft textile jitter, overcome the traditional method detection accuracy was not high. Experimental results show that this method can effectively avoid the weft jitter, making accurate detection of the weft fiber cut, and achieved satisfactory results.

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Deep Learning for the Detection and Recognition of Rail Defects in Ultrasound B-Scan Images

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211021547 ◽

2021 ◽

pp. 036119812110215

Author(s):

Zhengxing Chen ◽

Qihang Wang ◽

Kanghua Yang ◽

Tianle Yu ◽

Jidong Yao ◽

...

Keyword(s):

Deep Learning ◽

Image Recognition ◽

Defect Detection ◽

High Performance ◽

Image Data ◽

Small Scale ◽

Detection Accuracy ◽

Data Set ◽

Detection Rates ◽

B Scan Images

Rail defect detection is crucial to rail operations safety. Addressing the problem of high false alarm rates and missed detection rates in rail defect detection, this paper proposes a deep learning method using B-scan image recognition of rail defects with an improved YOLO (you only look once) V3 algorithm. Specifically, the developed model can automatically position a box in B-scan images and recognize EFBWs (electric flash butt welds), normal bolt holes, BHBs (bolt hole breaks), and SSCs (shells, spalling, or corrugation). First, the network structure of the YOLO V3 model is modified to enlarge the receptive field of the model, thus improving the detection accuracy of the model for small-scale objects. Second, B-scan image data are analyzed and standardized. Third, the initial training parameters of the improved YOLO V3 model are adjusted. Finally, the experiments are performed on 453 B-scan images as the test data set. Results show that the B-scan image recognition model based on the improved YOLO V3 algorithm reached high performance in its precision. Additionally, the detection accuracy and efficiency are improved compared with the original model and the final mean average precision can reach 87.41%.

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An Image Multi-scale Feature Recognition Method Based on Image Saliency

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2021.15.32 ◽

2021 ◽

Vol 15 ◽

pp. 280-287

Author(s):

Chengzhi Yang

Keyword(s):

Image Recognition ◽

Feature Recognition ◽

Feature Fusion ◽

Image Feature ◽

Detection Accuracy ◽

Recognition Method ◽

Salient Region Detection ◽

Adaptive Combination ◽

Scale Feature ◽

Multi Scale

Image recognition refers to the technology which processes, analyzes and understands images with computer so as to recognize various targets and objects of different patterns. To effectively combine image recognition and intelligent algorithm can enhance the efficiency of image feature analysis, improve the detection accuracy and guarantee real-time detection. In image feature recognition, the following problems exist: the description of accurate object features, object blockage, complex and changeable scenes. Whether these problems can be effectively solved has great significance in improving the stability and robustness of object recognition algorithm. This paper takes image salience as the fundamental framework, and makes in-depth study of the problems of effective object appearance description, multi-feature fusion and multi-feature adaptive combination. Then it proposes an image multi-scale feature recognition method based on image salience and it can better locate the saliency object in the image, and more evenly highlight the salient object and significantly suppress background noises. The experiment results prove that salient region detection algorithm can better stress the entire salient image.

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Morphology-Based Steels Automatic Counting Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.29-32.1907 ◽

2010 ◽

Vol 29-32 ◽

pp. 1907-1912

Author(s):

Wen Cheng Wang ◽

Fa Liang Chang

Keyword(s):

Mathematical Morphology ◽

Image Recognition ◽

Binary Image ◽

Experimental Results ◽

Counting Method ◽

Recognition Method ◽

Counting Problem ◽

Automatic Counting ◽

Binary Segmentation

In order to solve the automatic counting problem of steels, this paper has proposed a image recognition method based on mathematical morphology. It captured the tiling steels image by CCD firstly. Then, the image is sent to computer and preprocessed by using denoising operation and binary segmentation et al.. Finally, the binary image was thinned using hit and miss transform which based on morphology, and the number of steels was obtained. Experimental results showed that this method is convenient and can enhance the accuracy of the steels automatic counting.

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Weld Image Recognition Algorithm Based on Deep Learning

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420520047 ◽

2019 ◽

Vol 34 (08) ◽

pp. 2052004

Author(s):

Yan Li ◽

Miao Hu ◽

Taiyong Wang

Keyword(s):

Deep Learning ◽

Image Recognition ◽

Weld Seam ◽

Detection Method ◽

Automatic Detection ◽

Recognition Algorithm ◽

Image Texture ◽

Detection Accuracy ◽

Weld Defects ◽

Weld Inspection

As an important part of metal processing, welding is widely used in industrial manufacturing activities, and its application scenarios are very extensive. Due to technical limitations, the welding process always unavoidably leaves weld defects. Weld defects are extremely hazardous, and the work used must be guaranteed to be defect-free, regardless of the field. However, manual weld inspection has subjective factors such as inefficiency and easy missed detection, and although some automatic weld inspection methods have appeared, these traditional methods still do not meet actual demand in terms of detection time and detection accuracy. Therefore, there is a need for a higher quality weld image automatic detection method to replace the manual method and the traditional automatic detection method. In view of the above, this paper proposes a weld seam image recognition algorithm based on deep learning. The Adam adaptive moment estimation algorithm is chosen as the backpropagation optimization algorithm to accelerate the training of convolutional neural networks and design an independent adaptive learning rate. Through the simulation of the collected 4500 tube images, the adaptive threshold-based method is used for weld seam extraction. The algorithm proposed in this paper is compared with the weld seam recognition method based on image texture feature value distribution (ITFVD) and the SUSAN-based weld defect target detection method. The results show that the proposed method can identify weld defects in a short time on different sizes of weld images, and can further detect the type of weld defects. In addition, the method in this paper is better than the other two methods in the false detection rate, recall rate and overall recognition accuracy, which shows that the experimental results have achieved the expected results.

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The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology

Frontiers in Psychology ◽

10.3389/fpsyg.2021.634993 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yangfan Tong ◽

Weiran Cao ◽

Qian Sun ◽

Dong Chen

Keyword(s):

Deep Learning ◽

Image Recognition ◽

Human Body ◽

Recognition System ◽

Detection Accuracy ◽

Practical Applications ◽

Television Production ◽

Film And Television ◽

Computing Performance ◽

Tv Production

As the development of artificial intelligence (AI) technology, the deep-learning (DL)-based Virtual Reality (VR) technology, and DL technology are applied in human-computer interaction (HCI), and their impacts on modern film and TV works production and audience psychology are analyzed. In film and TV production, audiences have a higher demand for the verisimilitude and immersion of the works, especially in film production. Based on this, a 2D image recognition system for human body motions and a 3D recognition system for human body motions based on the convolutional neural network (CNN) algorithm of DL are proposed, and an analysis framework is established. The proposed systems are simulated on practical and professional datasets, respectively. The results show that the algorithm's computing performance in 2D image recognition is 7–9 times higher than that of the Open Pose method. It runs at 44.3 ms in 3D motion recognition, significantly lower than the Open Pose method's 794.5 and 138.7 ms. Although the detection accuracy has dropped by 2.4%, it is more efficient and convenient without limitations of scenarios in practical applications. The AI-based VR and DL enriches and expands the role and application of computer graphics in film and TV production using HCI technology theoretically and practically.

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