The invention provides a method and equipment for recognizing multiple attributes of target objects, a medium and a neural network. According to the method, a plurality of attribute features of a target object can be synchronously recognized by a plurality of full connection layers, and the quantity of the full connection layers can be set according to the quantity of the to-be-recognized attribute features, so that the modification is convenient and the flexibility is high. The layer number of the neural network does not exceed 34, so that the processing time of a single picture can be rapidly shortened while the precision is ensured.

Method and equipment for recognizing multiple attributes of target objects, medium and neural network

The invention discloses a method for realizing convolution neural network based on a memristor, which comprises the following steps: preparing a convolution neural network to be trained composed of convolution + pooling layer and full connection layer, inputting image information of a training set to obtain output value of a full connection layer; according to the deviation between the output value of all connection layers of the convolution neural network to be trained and the standard information, the convolution neural network to be trained being trained by the back propagation function; when the training times are reached, whether the training precision is up to the standard or not being judged If the training precision is not up to the standard, the convolutional neural network beingtrained again until the precision is up to the standard A method for obtain a desired convolution neural network and that like The remarkable effects are: the influence on precision, difficulty ofpreparation and time is reduced in hardware; a more advanced multi-layer convolution neural network is realized The method for realizing convolution neural network based on a memristor is not easy tobe affected by hardware noise, preparation difficulty and time, and after the application of complex can show a stronger adaptive capacity

A method for realizing convolution neural network based on memristor

The invention provides an image processing method and device based on a full convolutional neural network, and the method comprises the steps: carrying out the data refinement of the image data of a to-be-processed image, converting the to-be-processed image subjected to the data refinement into a single-channel gray level image corresponding to three primary colors, carrying out the image convolution, and obtaining a feature map of the to-be-processed image; and performing image deconvolution on the feature map of the to-be-processed image to restore the feature map to a target image which has the same resolution as the to-be-processed image and marks the category of the target object. The data refinement enables the pixel value of each pixel point in the image to be more accurate, and more detail features can be extracted when features are extracted through convolution. The three-channel image is converted into the three single-channel grayscale images corresponding to the three primary colors for convolution, and compared with direct convolution of the three-channel image, the loss of image features can be reduced, so that the final image processing result is more accurate, and the method can be well used for processing the image under the weak illumination condition.

Image processing method and device based on full convolutional neural network

The invention discloses a foreign matter detection method for an overhead transmission line based on image processing. The method comprises the following steps: shooting three resolution images of each overhead transmission line containing foreign matters; Preprocessing each image; Setting a corresponding foreign matter label for each preprocessed image; Constructing a multi-scale convolutional neural network model; Taking the three resolution images corresponding to each foreign matter label as input values in parallel, and inputting the input values into a multi-scale convolutional neural network model; Training the convolutional neural network model to obtain a trained convolutional neural network model; Shooting and preprocessing the overhead transmission line image to be detected in the same way, and inputting three resolution images of the overhead transmission line to be detected into the trained convolutional neural network model in parallel; And outputting the foreign matter type in the overhead transmission line image to be detected.

A foreign matter detection method for an overhead transmission line based on image processing

The invention discloses a crop disease recognition method based on a CNN (Convolutional Neural Network) and a multi-video image, and belongs to the technical field of image processing and machine learning. The method comprises the steps: expanding an original color disease leaf image training set collected through the agricultural IOT (Internet of Things), and obtaining an expanded training set; constructing a multi-channel CNN, training the multi-channel CNN through the expanded training set, and obtaining a crop disease recognition model; inputting a crop disease leaf image of a to-be-recognized crop into a crop disease recognition model for image classification, and obtaining the type of the disease of the to-be-recognized crop. The method can improve the accuracy of the crop disease recognition model, and can solve a problem that the accuracy of a conventional crop disease recognition method is lower in accuracy.

Crop disease recognition method based on CNN (Convolutional Neural Network) and multi-video image

The invention relates to a crop disease recognition algorithm based on a three-channel convolutional neural network. The crop disease recognition algorithm is mainly and technically characterized by comprising the steps image preprocessing is conducted; RGB colored diseased leaf images are decomposed into an R channel, a G channel and a B channel to serve as inputs of the convolutional neural network; a three-channel convolutional neural network model is constructed; weights and offset parameters are initialized; and the trained convolutional neural network is retrained through a training database, therefore, the trained model parameters can be fit with a current database, and more abstract features of the images are extracted for image recognition. The crop disease recognition algorithm is reasonable in design, experiment verification is conducted on an apple diseased leaf image database and a cucumber diseased leaf image database, cucumber diseased leaf image features can be accurately described, therefore, the obtained feature values of the target pixel can be more representative, and the recognition rate is increased; and description of the obtained feature values on feature information of cucumber diseased leaf images is comprehensive, and therefore cucumber diseased leaf image recognition can be effectively conducted.

Crop disease recognition algorithm based on three-channel convolutional neural network

The invention provides a plant identification method based on elliptical Fourier descriptors and weighted sparse representation. The plant identification method is mainly and technically characterized by comprising the steps that leaf images are preprocessed, wherein all the colored leaf images are converted into grayscale images, the leaf images are separated from the background through an Otsu segmentation algorithm and converted into binary images, and small holes of the leaf images are eliminated through an erosion algorithm; edge detection is conducted by adopting a Canny edge detector; centroids of boundaries are calculated; the Fourier descriptors are calculated; a complete dictionary is constructured, wherein Fourier descriptor vectors of all leaf image data sets are divided into training sets and test sets, and the complete dictionary is composed of the Fourier descriptor vectors of all the training sets; and optimization is conducted by a weighted sparse representation classifier. According to the plant identification method, by adopting the elliptical Fourier descriptors, good robustness is achieved on noise and other factors, and by applying the weighted sparse representation classifier (WSRC) to plant species identification and particularly to a low-dimension space, the identification rate is obviously increased.

Plant identification method based on elliptical Fourier descriptors and weighted sparse representation

The invention discloses a simulated environment detecting system. The simulated environment detecting system comprises a simulation simulator, a system server and a user application terminal; the simulation simulator simulates the environmental data and transmits the environmental data to the system server; the system server stores the environmental data; the simulation simulator is electrically connected to the system server for data interaction; and the user application end is wirelessly connected to the system server for data interaction. The invention discloses a simulated environment detecting system, which is an information system that can enable a user to detect the environmental information, and can implement multiple functions such as registration and login of the user, viewing the environmental information, adding environmental equipment, and refreshing the environmental data. After a whole set of careful test of the system, the system functions basically meet the user needsand complete the system requirements formulated by the system in advance.

Zhang Chuanlei

Papers

Crop disease recognition algorithm based on three-channel convolutional neural network

Plant identification method based on elliptical Fourier descriptors and weighted sparse representation

Simulated environment detecting system