Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review

Defect detection of injection molding products on small datasets using transfer learning

Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review

AFFU-Net: Attention feature fusion U-Net with hybrid loss for winter jujube crack detection

ABSTRACT Earth observation using remote sensing data is a trending research field across the globe. In this perspective, image registration is a mandatory data processing step for any kind of time series data analytics. Feature-based image registration is one of the prominent categories for superimposing multi-temporal and multi-modal images over each other. The paper provides a comprehensive and comparative survey of feature detection/description techniques for remote sensing images. In addition, outlier removal algorithms are explored in detail to generate putative keypoint correspondences for accurate transformation parameter estimation. The experiments are conducted on multiple remote sensing image pairs comprising visible, Synthetic Aperture Radar (SAR) and infrared images that provide diverse characteristics of the feature target. The co-registration accuracy is quantified for all possible combinations of feature detection/description with outlier removal, and best amalgamation is visually verified to check sub-pixel spatial alignment by swiping multi-temporal or multi-modal images over each other. It has been found that SIFT performs better in optical image registration, whereas A-KAZE feature detection has an upper edge at SAR image registration task. Marginalizing Sample Consensus (MAGSAC) and Mode Guided (MG) based outlier removal techniques achieves better pruning performance for multi-temporal optical and SAR images respectively. The comparative evaluation indicates that Motion Smoothness Constraint (MSC) optimization shows optimal performance for multi-modal remote sensing images. The best possible Root Mean Square Error (RMSE) achieved for multi-temporal optical images is 0.44 pixel whereas for multi-temporal SAR images, it is 0.46 pixels. The RMSE achieved for multi-modal images is 0.48 pixel using combination of SIFT with MSC.

Feature based remote sensing image registration techniques: a comprehensive and comparative review

Quality grading of jujubes using composite convolutional neural networks in combination with RGB color space segmentation and deep convolutional generative adversarial networks

Although convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection The performance of jujube defect detection mainly depends on the feature extraction and the classifier used Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 9415% The model has good stability and high recognition accuracy in complex environments

/pdf/classification-of-jujube-defects-in-small-data-sets-based-on-339zu69ov9.pdf

Classification of jujube defects in small data sets based on transfer learning

Learning fusion feature representation for garbage image classification model in human–robot interaction

AGCNNs: Attention-guided convolutional neural networks for infrared head pose estimation in assisted driving system

An important aspect of identifying whether items are likely to be forged or infringe upon a copyright is the use of an anti-counterfeiting pattern because it can help in determining and detecting forged anti-counterfeiting labels and patterns. At present, most anti-counterfeiting systems require special materials or large samples of images for training. Once they are geometrically attacked or forged by printing and scanning, they will be completely invalid. Therefore, this paper proposes an anti-counterfeiting system that uses a single corresponding feature difference sequence of the key regions for statistical analysis. The aim of our technology is to use the inks to generate random subtle texture patterns, and construct a supervised and guided segmentation algorithm and bone width transformation algorithm to locate the key regions of the sample images, which is used to identify the authenticity of the inspected product. The experiment shows that the system not only has high anti-counterfeiting performance and good robustness but also provides a convenient and practical idea for anti-counterfeiting technology.

A system for identifying an anti-counterfeiting pattern based on the statistical difference in key image regions

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