Digital Image Art Style Transfer Algorithm Based on CycleGAN

With the continuous development and popularization of artificial intelligence technology in recent years, the field of deep learning has also developed relatively rapidly. The application of deep learning technology has attracted attention in image detection, image recognition, image recoloring, and image artistic style transfer. Some image art style transfer techniques with deep learning as the core are also widely used. This article intends to create an image art style transfer algorithm to quickly realize the image art style transfer based on the generation of confrontation network. The principle of generating a confrontation network is mainly to change the traditional deconvolution operation, by adjusting the image size and then convolving, using the content encoder and style encoder to encode the content and style of the selected image, and by extracting the content and style features. In order to enhance the effect of image artistic style transfer, the image is recognized by using a multi-scale discriminator. The experimental results show that this algorithm is effective and has great application and promotion value.

E-Commerce Picture Text Recognition Information System Based on Deep Learning

For the accuracy requirements of commodity image detection and classification, the FPN network is improved by DPFM ablation and RFM, so as to improve the detection accuracy of commodities by the network. At the same time, in view of the narrowing of channels in the application of traditional MWI-DenseNet network, a new GTNet network is proposed to improve the classification accuracy of commodities.The results show that at different levels of evaluation indexes, the dpFPN-Netv2 algorithm improved by DPFM + RFM fusion has higher target detection accuracy than RetinaNet-50 algorithm and other algorithms. And the detection time is 52 ms, which is significantly lower than 90 ms required for RetinaNet-50 detection. In terms of target recognition, compared with the traditional MWI-DenseNet neural network, the computation amount of the improved MWI DenseNet neural network is significantly reduced under different shunt ratios, and the recognition accuracy is significantly improved. The innovation of this study lies in improving the algorithm from the perspective of target detection and recognition, so as to change the previous improvement that only can be made in a single way.

Agricultural harvesting using integrated robot system

In today's competitive world, robot designs are developed to simplify and improve quality wherever necessary. The rise in technology and modernization has led people from the unskilled sector to shift to the skilled sector. The agricultural sector's solution for harvesting fruits and vegetables is manual labor and a few other agro bots that are expensive and have various limitations when it comes to harvesting. Although robots present may achieve harvesting, the affordability of such designs may not be possible by small and medium-scale producers. The integrated robot system is designed to solve this problem, and when compared with the existing manual methods, this seems to be the most cost-effective, efficient, and viable solution. The robot uses deep learning for image detection, and the object is acquired using robotic manipulators. The robot uses a Cartesian and articulated configuration to perform the picking action. In the end, the robot is operated where carrots and cantaloupes were harvested. The data of the harvested crops are used to arrive at the conclusion of the robot's accuracy.

Recaptured images detection algorithm based on frequency domain transformation

With the raising popularity of digital devices in the current society, the present image detection system is becoming a great threaten. Especially the appearance of the recaptured images. It can be used in traditional invalid digital image detection algorithm. There is a new algorithm in this paper is presented to detect the recaptured and real image. The algorithm obtains low-frequency images, directional filtering images and high-frequency images by multiple application frequency domain filtering. Then the proposed algorithm analyzes the directional filtering images and high-frequency images by means of LBP algorithm to extract features. At last, the recaptured images were classified by the SVM. The experimental results demonstrated the algorithm in this paper could be effectively identify in the recaptured images.

DISEASE IDENTIFICATION IN CROPS USING DEEP LEARNING MODELS

Food is one of the most fundamental necessities and is crucial for survival. Loss of the food source due to pest infestation attributes towards destroying one-fifth of the yearly worldwide crop yield. The past few decades have witnessed a burgeoning trend of using computerized methods for discerning various diseases found in crops. The main advantage of digitizing the detection process is that it eliminates the errors and miscalculations associated with manual detection. With the advent of Object Detection and Artificial Intelligence, malady detection has not only been rapid but has also maintained the expected level of accuracy. The concepts and models of deep learning have been efficaciously applied and used to identify as well as classify plant diseases. In the scope of this research paper, we present a comprehensive digitized approach to detect plant diseases by utilizing image detection, computer vision, and deep learning models like the Convolutional neural networks, Inception model, and the Visual Geometry Group (VGG16) model. In addition to this, the performance of the above-mentioned models has been evaluated by the virtue of metrics like f1 score, accuracy, precision, and recall.

Reliable Template Matching for Image Detection in Vision Sensor Systems

Template matching is a simple image detection algorithm that can easily detect different types of objects just by changing the template without tedious training procedures. Despite these advantages, template matching is not currently widely used. This is because traditional template matching is not very reliable for images that differ from the template. The reliability of template matching can be improved by using additional information (depths for the template) available from the vision sensor system. Methods of obtaining the depth of a template using stereo vision or a few (two or more) template images or a short template video via mono vision are well known in the vision literature and have been commercialized. In this strategy, this paper proposes a template matching vision sensor system that can easily detect various types of objects without prior training. To this end, by using the additional information provided by the vision sensor system, we study a method to increase the reliability of template matching, even when there is a difference in the 3D direction and size between the template and the image. Template images obtained through the vision sensor provide a depth template. Using this depth template, it is possible to predict the change of the image according to the difference in the 3D direction and the size of the object. Using the predicted changes in these images, the template is calibrated close to the given image, and then template matching is performed. For ease of use, the algorithm is proposed as a closed form solution that avoids tedious recursion or training processes. For wider application and more accurate results, the proposed method considers the 3D direction and size difference in the perspective projection model and the general 3D rotation model.