Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

Chen, Rui; Wang, Meiling; Lai, Yi

doi:10.1371/journal.pone.0235783

Cited by 17 publications

(9 citation statements)

References 30 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[8]. In 2020, Chen [9] extracted a variety of polyhedral three-dimensional structures in digital images. Under Roberts' continuous research, he pioneered the research of three-dimensional computer vision.…”

Section: Related Workmentioning

confidence: 99%

Using an Improved PSO-SVM Model to Recognize and Classify the Image Signals

Zhao

Wei

2021

Complexity

View full text Add to dashboard Cite

Image recognition is an important field of artificial intelligence. Its basic idea is to use computers to automatically classify different scenes in the acquired images, instead of traditional manual classification tasks. In this paper, through the analysis of rough set theory and artificial intelligence network, as well as the role of the two in image recognition, the rough set theory and artificial intelligence network are organically combined, and a network based on rough set theory and artificial intelligence network is proposed. Using BP artificial intelligence network model, improved BP artificial intelligence network model, and improved PSO-SVM model to identify and classify the extracted characteristic signals and compare the results, all reached 85% correct rate. The PCA and SVM are combined and applied to the MNIST handwritten digit collection for recognition and classification. At the data level, dimensionality reduction is performed on high-dimensional image data to compress the data. This greatly improves the performance of the algorithm, the recognition accuracy rate is as high as 98%, and the running time is shortened by about 90%. The model first preprocesses the original image data and then uses rough set theory to select features, which reduces the input dimension of the artificial intelligence network, improves the learning and recognition speed of the artificial intelligence network, and further improves the accuracy of recognition. The paper applies the model to handwritten digital image recognition, and the experimental results show that the model is effective and feasible. The system has the characteristics of easy deployment and easy maintenance and integration. Experiments show that the system has good time characteristics in the process of multialgorithm parallel image fusion processing.

show abstract

Section: Related Workmentioning

confidence: 99%

Using an Improved PSO-SVM Model to Recognize and Classify the Image Signals

Zhao

Wei

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…Ordinary convolution is the process of sliding the convolution kernel over the image and finally completing the computation of gray values of all image pixels through a series of matrix operations. Transposed convolution implements the sampling operation in the reverse direction of ordinary convolution and is widely used in semantic segmentation [ 41 ], image recognition [ 42 ], etc. Dilated convolution, also known as hole convolution, injects holes into the convolution kernel to increase the perceptual field [ 43 ] of the model for better feature extraction.…”

Section: Related Workmentioning

confidence: 99%

A High-Performance Day-Age Classification and Detection Model for Chick Based on Attention Encoder and Convolutional Neural Network

Ren

Huang

Wang

et al. 2022

Animals

View full text Add to dashboard Cite

Thanks to the boom of computer vision techniques and artificial intelligence algorithms, it is more available to achieve artificial rearing for animals in real production scenarios. Improving the accuracy of chicken day-age detection is one of the instances, which is of great importance for chicken rearing. To solve this problem, we proposed an attention encoder structure to extract chicken image features, trying to improve the detection accuracy. To cope with the imbalance of the dataset, various data enhancement schemes such as Cutout, CutMix, and MixUp were proposed to verify the effectiveness of the proposed attention encoder. This paper put the structure into various mainstream CNN networks for comparison and multiple ablation experiments. The final experimental results show that by applying the attention encoder structure, ResNet-50 can improve the accuracy of chicken age detection to 95.2%. Finally, this paper also designed a complete image acquisition system for chicken houses and a detection application configured for mobile devices.

show abstract

“…In the aspect of target detection, some are based on the R-CNN framework and YOLO v3 algorithm to locate and identify the grid points and crops, and realize the positioning and position calibration within the spatial range [4,5]; some intelligent object surface detection systems based on the fast R-CNN algorithm can realize the surface detection and detect the location of geometrically complex products [6]. Further, there are goods detection and recognition based on multilevel CNN, deep CNN, and deep neural networks to realize the detection of unsafe articles in subway security inspection and operation, and improve the recognition accuracy [7][8][9]. Furthermore, the random maximum interval combination model constructed by a weak information part and R-CNN detection are used to realize the accurate recognition of objects [10,11].…”

Section: Related Work 21 Target Identification and Classificationmentioning

confidence: 99%

Deep-Learning-Based Accurate Identification of Warehouse Goods for Robot Picking Operations

Liu

Zhao

et al. 2022

Sustainability

View full text Add to dashboard Cite

In order to explore the application of robots in intelligent supply-chain and digital logistics, and to achieve efficient operation, energy conservation, and emission reduction in the field of warehousing and sorting, we conducted research in the field of unmanned sorting and automated warehousing. Under the guidance of the theory of sustainable development, the ESG (Environmental Social Governance) goals in the social aspect are realized through digital technology in the storage field. In the picking process of warehousing, efficient and accurate cargo identification is the premise to ensure the accuracy and timeliness of intelligent robot operation. According to the driving and grasping methods of different robot arms, the image recognition model of arbitrarily shaped objects is established by using a convolution neural network (CNN) on the basis of simulating a human hand grasping objects. The model updates the loss function value and global step size by exponential decay and moving average, realizes the identification and classification of goods, and obtains the running dynamics of the program in real time by using visual tools. In addition, combined with the different characteristics of the data set, such as shape, size, surface material, brittleness, weight, among others, different intelligent grab solutions are selected for different types of goods to realize the automatic picking of goods of any shape in the picking list. Through the application of intelligent item grabbing in the storage field, it lays a foundation for the construction of an intelligent supply-chain system, and provides a new research perspective for cooperative robots (COBOT) in the field of logistics warehousing.

show abstract

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

Cited by 17 publications

References 30 publications

Using an Improved PSO-SVM Model to Recognize and Classify the Image Signals

Using an Improved PSO-SVM Model to Recognize and Classify the Image Signals

A High-Performance Day-Age Classification and Detection Model for Chick Based on Attention Encoder and Convolutional Neural Network

Deep-Learning-Based Accurate Identification of Warehouse Goods for Robot Picking Operations

Contact Info

Product

Resources

About