An improved residual network model for image recognition using a combination of snapshot ensembles and the cutout technique

Intelligent detection of imperfect wheat grains based on machine vision is of great significance to correctly and rapidly evaluate wheat quality. There is little difference between the partial characteristics of imperfect and perfect wheat grains, which is a key factor limiting the classification and recognition accuracy of imperfect wheat based on a deep learning network model. In this paper, we propose a method for imperfect wheat grains recognition combined with an attention mechanism and residual network (ResNet), and verify its recognition accuracy by adding an attention mechanism module into different depths of residual network. Five residual networks with different depths (18, 34, 50, 101, and 152) were selected for the experiment, it was found that the recognition accuracy of each network model was improved with the attention mechanism, and the average recognition rate of ResNet-50 with the addition of the attention mechanism reached 96.5%. For ResNet-50 with the attention mechanism, the optimal learning rate was further screened as 0.0003. The average recognition accuracy reached 97.5%, among which the recognition rates of scab wheat grains, insect-damaged wheat grains, sprouted wheat grains, mildew wheat grains, broken wheat grains, and perfect wheat grains reached 97%, 99%, 99%, 95%, 96%, and 99% respectively. This work can provide guidance for the detection and recognition of imperfect wheat grains using machine vision.

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Maize Disease Recognition Based On Image Enhancement And OSCRNet

10.21203/rs.3.rs-871678/v1 ◽

2021 ◽

Author(s):

Hongji Zhang ◽

Zhou Guoxiong ◽

Aibin Chen ◽

Jiayong Li ◽

Mingxuan Li ◽

...

Keyword(s):

Image Enhancement ◽

Network Model ◽

Image Recognition ◽

Spatial Information ◽

Incident Light ◽

Learning Disorders ◽

Identification Accuracy ◽

Data Set ◽

Maize Leaf ◽

Multi Scale

Abstract Background: Under natural light irradiation, there are significant challenges in the identification of maize leaf diseases because of the difficulties in extracting lesion features from constantly changing environments, uneven illumination reflection of the incident light source and many other factors.Results: In the present paper, a novel maize image recognition method was proposed. Firstly, an image enhancement framework of the maize leaf was designed, and a multi-scale image enhancement algorithm with color restoration was established to enhance the characteristics of the maize leaf in a complex environment and to solve the problems of high noise and blur of maize images. Subsequently, an OSCRNet maize leaf recognition network model based on the traditional ResNet backbone architecture was designed. In the OSCRNet maize leaf recognition network model, an octave convolution with characteristics to accelerate network training was adopted, reducing unnecessary redundant spatial information in the maize leaf images. Additionally, a self-calibrated convolution with multi-scale features was employed to realize the interactions of different feature information in the maize leaf images, enhance feature extraction, and solve the problems of similarity of maize disease features and easy learning disorders. Concurrently, batch normalization was employed to prevent network overfitting and enhance the robustness of the model. The experiment was conducted on the maize leaf image data set. The highest identification accuracy of rust, grey leaf disease, northern fusarium wilt, and healthy maize was 94.67%, 92.34%, 89.31% and 96.63%, respectively. Conclusions: The aforementioned methods were beneficial in solving the problems of slow efficiency, low accuracy and image recognition training, and also outperformed other comparison models. The present method demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases.

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An FPGA Implementation of Finite Physical Quantity Neural Network

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2003.p0136 ◽

2003 ◽

Vol 15 (2) ◽

pp. 136-142

Author(s):

Takashi Sotohebo ◽

◽

Minoru Watanabe ◽

Funtinori Kobayashi

Keyword(s):

Neural Network ◽

Network Model ◽

Image Recognition ◽

Physical Quantity ◽

Neural Network Model ◽

Field Programmable Gate Array ◽

High Speed ◽

Density Field ◽

Experimental Results ◽

Field Programmable

We propose installing a finite physical quantity neural network model on a high-density field programmable gate array (FPGA) at high speed by reducing multipliers. We could thereby downsize circuits without loss of precision. We evaluated its installation and experimental results for image recognition.

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