The invention discloses a road damage detection method and device based on deep learning image classification. The method comprises the following steps: Step one, collecting images of different road scene types and position information to form a sample image set and marking the road scene types; Step two, marking the road scene types; Step three, selecting a classification network model accordingto the need, and training the model; Step four, inputting an image of a road to be detected into the trained deep neural network model to obtain a classification result; and Step five, if a classification result is road damage type, confirming the road segment position information corresponding to the acquired image, and outputting prompt information of the road damage type and the position information. The method improves the accuracy of road damage detection, does not need to set a detection threshold value, and has high real-time performance and diversified mounting position selection.

Road damage detection method and device based on deep learning image classification

The invention discloses a road surface crack defect identification method based on combination of image processing and a convolutional neural network. The method comprises the following steps: step 1,data preparation: acquiring 2000 1024 * 512 road grayscale images; step 2, image calibration: classifying the collected road images, calibrating the images without cracks as 0 folder, and calibratingthe images with cracks as 1 folder; step 3, carrying out picture preprocessing on the calibrated picture, and the unified size being 1024*512; step 4, establishing a core convolutional neural networkalgorithm frame AsphaltCackNet; step 5, generating a principle of a feature extractor for feature mapping; step 6, dividing an image data set for training and testing; step 7, training the data set and calculating a cost function; step 8, a learning method of the neural network; step 9, randomly initializing and losing; step 10, performing parallel computing; step 11, obtaining a training result,and performing testing and evaluation. Under the condition that the width and the height of an original image are not changed, perfect pixel accuracy can be ensured.

Road surface crack defect detection method based on combination of image processing and convolutional neural network

The invention relates to a SAR image classification method based on a multi-feature and a non-negative automatic encoder, belonging to the technical field of image processing. The method comprises steps: extracting Spatial Features of SAR Image Blocks Based on Gray Level Gradient Co-occurrence Matrix; feature Extraction of SAR Image Block Transform Domain Based on Two-Dimensional Gabor Transform;combining the spatial domain features and the transform domain features of an image block; the training sample set and test sample set of SAR image block being selected. training multi-layer non-negative automatic encoder and softmax classifier with training sample set; classification being based on the trained non-negative automatic encoder network, so that a classification result diagram is obtain. The invention combines the spatial information of the SAR image and the information of the transform domain, obtains the multi-dimensional features of the SAR image, optimizes the features by using a non-negative automatic encoder, improves the distinctiveness of the features, further effectively improves the accuracy of classification, and can be used for the classification of the ground objects of the high-resolution SAR image and the target recognition, and the like.

SAR image classification based on multi-feature and non-negative automatic encoder

The invention belongs to the technical field of image processing defect detection and discloses a small sample defect detection method based on image processing and artificial intelligence. The methodcomprises the following steps of: S1, image acquisition; and S2, image preprocessing detect defection. According to the image acquisition, a multi-position camera is installed to obtain the image ofeach position surface of a to-be-processed workpiece. The image preprocessing detect defection involves the defection of images without obvious texture features and texture defect detection. With thesmall sample defect detection method based on image processing and artificial intelligence adopted, the number of weights is greatly reduced; training parameters required by a network are reduced; thecomplexity of the network is reduced; actual use is more convenient; and the yield of workpieces can be more effectively and quickly detected.

Small sample defect detection method based on image processing and artificial intelligence

The invention discloses a grey-scale map energy value-based floor tile fluctuation defect detection method for a pedestrian walkway. The method comprises the following steps: acquiring a grey-scale map of an image frame in a video of a pedestrian walkway; determining a gray-scale abnormal region in the gray-scale map and a predetermined direction of the gray-scale abnormal region; calculating thelowest energy line along the predetermined direction in the gray scale abnormal region to serve as a gray scale abnormal line; inputting the plurality of continuous image frames marked with the gray abnormal lines into a convolutional neural network to obtain a feature map; and based on the feature map, a classification function being used to obtain a classification result of the image frame, andthe classification result being used to represent whether a floor tile fluctuation defect exists in a pedestrian walkway corresponding to the image frame. Therefore, the gray abnormal line in the pedestrian walkway image is determined by using the image segmentation method, and whether the abnormal line is the floor tile fluctuation defect on the pedestrian walkway is judged by using the deep neural network based on the gray abnormal line as the annotation feature.

Floor tile fluctuation defect detection method for pedestrian walkway based on grey-scale map energy value

The invention provides a road surface defect detection method based on texture feature extraction. The road surface defect detection method comprises the following steps that an image having a road surface defect is acquired and grayscale processing is performed so as to form a road surface defect grayscale image; texture feature extraction is performed on the road surface defect grayscale image,the feature values are extracted to form texture feature vectors and the road surface defect grayscale images represented by the texture feature vectors of the same defect class are equally divided soas to form a training set and a test set; high-dimensional abstract feature extraction is performed on the training set through a stack type auto-encoder; and a softmax layerer logical classificationlayer is stacked in the stack type auto-encoder to form a deep neural network, the high-dimensional abstract features are trained through the deep neural network and classification and identificationof the road surface grayscale images of the test set are completed. The road surface defect can be accurately detected and the accuracy of the result can be enhanced.

Road surface defect detection method based on texture feature extraction

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Road surface defect detection method based on texture feature extraction