752 results about "Saliency map" patented technology

The present invention discloses an edge-guided RGBD underwater salient object detection method with multi-attention, which includes the steps of: S1 performing channel-dimension preprocessing on RGB image and depth map and serially connecting them to obtain an RMD image; S2 inputting the RMD image into a trained salient object detection model including an encoder structure and a decoder structure; and S3 obtaining salient images and edge images output from the salient object detection model as object detection results, in which the encoder generates feature maps with reduced resolution layer by layer using the RMD image based on a residual neural network, and the decoder structure includes an edge detection branch used to fuse the feature maps layer by layer, and a saliency detection branch used to fuse the feature maps and edge feature maps layer by layer. The present invention preprocesses the input image with underwater imaging features, and uses an edge prediction image to guide multi-layer salient object detection, thereby enhancing edge details of the salient image.

The invention discloses an optical remote sensing image salient target detection method based on progressive attention enhancement, and belongs to the technical field of computer vision. The method comprises the following steps: preprocessing an original data set; inputting the preprocessed image into a hierarchical progressive fusion encoder, capturing a global irregular topological structure and local fine-grained image details, and realizing cross-hierarchical feature fusion; inputting the output characteristics of the encoder into a global context enhancement module, and capturing multi-level context information by adopting a parallel multi-branch structure; and inputting the output features of the hierarchical progressive fusion encoder and the global context enhancement module into a multi-scale progressive attention enhancement decoder, carrying out hierarchical decoding on the input features by adopting a saliency-guided attention mechanism, and gradually aggregating deep semantic information and shallow detail features to realize coarse-to-fine progressive optimization, so as to improve the robustness of the multi-scale progressive attention enhancement decoder. And finally generating a saliency map. The method can effectively improve the processing performance of an irregular topological structure and a complex context relationship in the optical remote sensing image.

The invention discloses a pathological section virtual staining image generation method based on a diffusion model, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, passing through Hamp; e dyeing serves as a source image data set, and pathological section images subjected to IHC dyeing serve as a target image data set; preprocessing the source image, and converting the source image into a corresponding YUV color space; extracting a saliency map and a gradient map of the source image, and generating a weighted attention map; building a diffusion model by using a neural network, and inputting the preprocessed source image for fine tuning; repeatedly iterating until the loss function converges, and ending the training; and inputting a weighted attention map to perform reasoning of virtual dyeing. According to the method, weighted attention of a pathological section image is added in a reasoning part to serve as an image prompt input network, so that the cell structure of a finally generated image is controlled not to be changed, and meanwhile, color features of a target dyed image are reserved and emphasized; and the accuracy and the reconstruction effect of the generated virtual dyeing image are improved.

The invention relates to the technical field of industrial machine vision detection and image processing, in particular to an automobile part production mold surface smoothness detection system based on image enhancement, which comprises a data acquisition module used for acquiring an original grayscale image of the surface of an automobile part mold; performing low-pass filtering processing on the original grayscale image to eliminate imaging thermal noise; the manifold reconstruction module is used for constructing a structure tensor field; reversely deducing a pseudo-curvature field of the mold surface; the adaptive enhancement module is used for generating a corrected image; constructing a texture orthotropic diffusion model; generating a texture reconstruction reference image; the surface metering module is used for calculating the difference between the corrected image and the texture reconstruction reference image and generating a defect saliency image; calculating the surface roughness value of the mold surface; according to the method, the problem that design textures and abnormal scratches are difficult to distinguish in the prior art is effectively solved, and the technical bottleneck that micro defects are easily missed in a complex geometric structure in traditional visual detection is overcome.

The invention relates to the technical field of intelligent navigation, financial science and technology and medical health, and discloses a navigation method, device and equipment based on multi-modal knowledge enhancement and a medium, and the method comprises the following steps: obtaining a navigation instruction, and analyzing the navigation instruction to obtain a target constraint, a spatial constraint and an entity constraint; generating a structured target description text based on the target constraint, the spatial constraint and the entity constraint; an input image is acquired, a semantic saliency map is generated based on the input image and the structured target description text, and the semantic saliency map comprises a plurality of candidate areas; calculating a score of each candidate region of the semantic saliency map based on a preset scene knowledge graph to obtain a fusion score; and performing path planning according to the fusion score of each candidate region to obtain a target path. Navigation can be realized without navigation data training, and meanwhile, a potential target can be focused according to semantics, so that the navigation precision is improved.

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

The invention discloses an electric power operation unmanned aerial vehicle safety supervision target identification and detection method, system and device, and a medium. The method comprises the following steps: carrying out electric power operation risk identification based on environment characteristics and task attributes of an electric power operation area; configuring a monitoring frequency, and performing data acquisition on a power operation area; constructing a target region segmentation device based on a predetermined operation monitoring target, performing target segmentation on the monitoring image, and outputting a region image set; traversing and comparing the regional image set acquired under the current monitoring node with the historical regional image set acquired under the previous adjacent monitoring node, selecting images with significant differences, and performing point cloud fitting to construct a three-dimensional target simulation model; and building an anomaly identification plug-in based on a predetermined monitoring index and the operation early warning area, carrying out anomaly detection on the three-dimensional target simulation model, and outputting an operation detection result. According to the scheme, global intelligent monitoring of electric power high-altitude operation is achieved, potential safety hazards are found in time, and the timeliness and accuracy of risk identification are remarkably improved.

The invention relates to the technical field of image processing, in particular to a high-voltage electrical equipment surface defect identification method based on image processing. The method comprises the following steps: analyzing the gradient of pixel points in a to-be-analyzed image of a to-be-detected area on the surface of the high-voltage electrical equipment to obtain the weight of each pixel point; weighting the gray value of each pixel point in the to-be-analyzed image subjected to Laplacian filtering by using the weight of each pixel point of the to-be-analyzed image to obtain a first feature map; calculating a local standard deviation of each pixel point in the to-be-analyzed image so as to obtain a first parameter and a second parameter; constructing two Gaussian kernels based on the first parameter and the second parameter to filter the to-be-analyzed image to obtain a second feature map; fusing the first feature map and the second feature map of the to-be-analyzed image to obtain a defect saliency map of the to-be-analyzed image; and recognizing a surface defect area of the high-voltage electrical equipment based on the defect saliency map of each to-be-analyzed image. According to the invention, the accuracy of high-voltage electrical equipment surface defect identification can be improved.

The invention discloses a multi-modal image matching method and system based on saliency graph structure enhancement, and belongs to the field of image processing. The method comprises the following steps: firstly, innovatively constructing a pixel-level saliency confidence graph for measuring the matching potential of each region, and guiding an attention mechanism to be dynamically focused on a key region in a graph structure through the graph; secondly, multi-scale structure features and semantic segmentation information are fused, and the semantic perception ability of feature expression is enhanced; and finally, constructing two heterogeneous graph structures of an in-image structure graph and an inter-image semantic guidance graph, and realizing global-local information enhancement and cross-modal semantic alignment on the graph structures by introducing a self-attention and cross-attention mechanism of saliency modulation, so that the matching precision and stability are remarkably improved, and the matching accuracy is improved. And semi-dense matching of multi-modal images is realized.

The invention discloses an unmanned aerial vehicle outdoor inspection method based on multi-modal fusion, and the method comprises the following steps: obtaining the inspection data of an unmanned aerial vehicle, and carrying out the feature extraction and space-time alignment; calling a GNPDE algorithm, and constructing a multi-modal field mapping layer; constructing a structure-thermal field joint graph structure, and establishing a double-branch coupling solution structure; constructing a topological adaptive edge weight adjustment module, and dynamically modulating the edge weight by adopting a physical modulation function; introducing an energy conservation constraint layer, and executing constraint solution; a multi-scale PDE evolution algorithm subset is quoted and combined, and a scale weight sharing mechanism is adopted to complete cross-scale joint optimization; constructing an abnormal residual reasoning module, and generating an abnormal significance map; and performing spatial registration and superposition on the abnormal saliency map and unmanned aerial vehicle inspection data, and outputting an equipment-level inspection report and a risk level conclusion. According to the invention, the inspection abnormity identification precision and the structure-thermal field reasoning stability are improved.

The present disclosure describes methods, systems, apparatus, and media for object identification and classification, utilizing multi-feature and multi-modal data. This includes shape, material, brand, price, odor, taste, tactility, and sound. The system integrates a server space for data processing, a querying device for iterative searches, and a data interface module for refining results. It features AI-driven image optimization, feature extraction, and pattern recognition, employing novel techniques for fusing multi-feature and multi-modal embeddings utilizing multi-head attention. Additionally, a linker module powered by two active learning with feedback loops AI models consolidates scattered data into a unified object information database. The system also employs novel AI algorithms for isolating the object of interest through a saliency map and semantic analysis, as well as for enhancing raw images with a GAN-autoencoder.

The invention discloses a dynamic and static bidirectional interactive cooperative micro-expression recognition method, which comprises the following steps of: firstly, introducing a double-attention guided feature fusion module into a dynamic branch, and fusing optical flow and pixel difference between a micro-expression starting frame and a vertex frame to represent a micro-expression motion feature; a gating cross-layer feature transfer mechanism is proposed to screen key motion features and inject the key motion features into a deep network in a cross-layer manner, so that noise interference is effectively suppressed, and the problem of micro-expression signal attenuation caused by continuous down-sampling is relieved; the method comprises the following steps: firstly, designing a static branch, secondly, designing a mixed attention Transform block in the static branch to generate a micro-expression saliency map and a token redistribution Transform block guided by the saliency map, accurately extracting multi-granularity static characteristics of a key area in a vertex frame, and finally, adopting a bidirectional interactive attention module to deeply and interactively fuse dynamic and static characteristics to realize accurate classification of micro-expressions.

The invention belongs to the technical field of image fusion, and particularly relates to an infrared and low-light-level image fusion improvement method based on rolling guide filtering, which comprises the following steps of S1, acquiring a low-light-level image and an infrared image, and preprocessing the infrared image for denoising; s2, converting to an IHS space to extract an intensity component; s3, decomposing the dual-source I component into a small-scale layer, a large-scale layer and a base layer by using rolling guide filtering; s4, performing small-scale layer fusion, and combining an absolute value maximization strategy with non-local similarity weighted denoising; s5, performing large-scale layer fusion, and performing joint sparse low-rank representation by adopting principal component analysis; s6, fusion of the base layer adopts a visual saliency mapping method; and S7, after IHS inverse transformation reconstruction, realizing multi-level fusion in combination with bilateral filtering detail enhancement and a guide filtering saliency map. According to the invention, based on IHS space and rolling guide filtering multi-scale decomposition, the effect of cooperative utilization of multi-modal information is improved, and the method effectively improves the problems of insufficient target detection precision and poor scene reconstruction robustness in a dark environment.

The invention relates to the technical field of printed matter quality detection and computer vision and the technical field of artificial intelligence systems in the production field, in particular to a color box printed matter printed pattern visual detection algorithm. The invention discloses a visual detection algorithm for a printing pattern of a color box printed matter. The visual detection algorithm comprises the following steps of image acquisition, preprocessing, feature extraction, reference comparison, defect evaluation and defect classification. According to the scheme, the accuracy and the real-time performance of complex printing pattern defect detection are improved. A light-weight multi-scale attention convolutional neural network and a self-learning mechanism are combined, defects in various printing patterns such as block colors, fine characters and graph gradient are effectively detected, and human intervention and misjudgment are reduced. A defect saliency map is generated through color normalization, geometric correction preprocessing and reference map comparison, an online misjudgment cache and incremental learning strategy is introduced, self-adaptive optimization of a model along with time is kept, and real-time high-precision quality monitoring of an offset printing color box production line is achieved.

The invention discloses a building outer wall defect detection method based on infrared polarization imaging, belongs to the field of image processing, and aims to improve the accuracy and robustness of building outer wall defect detection. The method comprises the steps of firstly collecting building outer wall surface image data, and constructing an image data set; designing a channel disturbance intensity factor and a mutual information measurement strategy, constructing a multi-channel disturbance enhancement attention module, and extracting a multi-scale feature map; constructing a position sensing context fusion module, and enhancing a feature fusion effect through a position modulation coefficient; constructing a geometric guidance saliency modeling module, and generating a structural perception saliency map through the geometric guidance deviation factor; the above modules are integrated to construct a defect detection model, a to-be-detected image is input into the model, a defect area is output, and accurate detection and positioning of building outer wall defects such as cracks, falling and bumps are achieved.

Embodiments of the present application provide a video sharpness assessment method, apparatus and device, a storage medium, and a product. The method comprises: converting a video image frame to be assessed into a gray-scale map, and performing discrete wavelet transform on the gray-scale map to obtain a high-frequency sub-band map; then performing saliency calculation on the gray-scale map to obtain a saliency map, scaling the saliency map, and converting the scaled saliency map into a weight matrix with the same size as the high-frequency sub-band map; and finally, on the basis of the weight matrix and the high-frequency sub-band map, calculating a total high-frequency energy value corresponding to the video image frame, so as to represent a sharpness assessment result of the video image frame. According to the scheme, saliency detection is introduced into video sharpness assessment, so that the video sharpness assessment focuses more on the part of an image that attracts human eyes' attention and is more in line with the human eye perception, and the accuracy of sharpness estimation is improved, thereby better feeding back the definition of videos.

The invention discloses an oil and gas pipeline magnetic flux leakage image defect identification method based on a deep attention mechanism, relates to the technical field of oil and gas pipeline detection, and is used for solving the problem of inaccurate identification of a magnetic flux leakage image of a pipeline elbow section. According to the method, the image frame sequence with the posture annotation is constructed through unified time reference and space coordinate mapping, and accurate alignment of the image and the pipeline position is achieved; a structural area marking graph and non-rigid normalization are introduced to compensate the distortion of the elbow section, and the image consistency is improved; constructing a structure perception embedded image on the distortion compensation image, fusing position, gradient and texture features to implement feature propagation and attention guidance, and generating a feature saliency map; a defect area is accurately extracted in combination with layered reconstruction and a two-stage judgment strategy; through continuous frame monitoring and recognition stability judgment, recognition parameters are adaptively updated, a closed loop from recognition to updating to verification is constructed, and the precision of oil and gas pipeline magnetic flux leakage image recognition under complex working conditions is enhanced.

The invention relates to the field of image processing, in particular to a box-type substation spraying quality detection method based on image processing, and the method comprises the steps: firstly obtaining a to-be-detected image, and calculating a pixel point structure tensor component after the to-be-detected image is preprocessed; extracting a gradient energy field and a gradient main direction angle field based on the component, and further calculating gradient radial consistency representing geometric characteristics of the defect; in combination with the to-be-detected image and the gradient energy field, respectively calculating saturation and flatness indexes representing optical reflection characteristics and intensity-gradient cross-correlation representing defect spatial forms; fusing the three features to generate a final saliency map; and finally, carrying out threshold segmentation and post-processing on the saliency map to obtain a detection result. According to the method, the physical spraying defect and the optical highlight reflection can be effectively distinguished, and the defect detection accuracy and robustness are remarkably improved.

The invention provides a construction method of a multi-task sight line estimation model. And constructing a multi-task sight estimation model based on frequency domain and airspace interaction. According to the construction method of the multi-task sight line estimation model, convolution of a receptive field with an image size is effectively realized in a frequency domain through Fourier transform, and global dependency can be captured so as to better utilize small and complex local features in an image; a multi-task learning strategy is adopted, and a plurality of related tasks are trained at the same time, so that the model can share useful information among different tasks, and the performance of each task is improved; end-to-end training of the multi-task sight line estimation model based on frequency domain and spatial domain interaction is completed by adopting a known sight line estimation data set and based on a saliency map representing a human eye region generated by left and right eye corner labels in the known sight line estimation data set; and testing the trained multi-task line-of-sight estimation model based on frequency domain and spatial domain interaction by adopting a test image in a known line-of-sight estimation data set.

The invention relates to the technical field of industrial vision, in particular to an aviation part crack detection and repair method which comprises the following steps: acquiring an aviation part optical image at a reference time point and an aviation part optical image at a to-be-detected time point; according to the method, logarithmic polar coordinate transformation is carried out on different time point images, rotation, scaling and translation parameters are extracted, an image registration relation is established, the structural consistency of time sequence images in a local area is enhanced, a structural tensor is constructed for each pixel neighborhood, the change characteristics of the dual-time-phase tensor are compared, a structural change saliency map is generated, and the structural change saliency map is obtained. Sensitive capture of a tiny deformation area is achieved, the responsiveness to an initial crack is improved, then a crack propagation interval is further refined into a main crack path in a self-adaptive threshold segmentation and skeleton extraction mode, the tip acutance of the crack is calculated in combination with tip contour information of a geometric boundary of the crack, and the initial crack is obtained. And quantitative support is provided for the crack danger degree.

The invention discloses an intelligent target detection method and device based on computer vision, and relates to the technical field of visual recognition. The method comprises the following specific implementation steps: step 1, detecting and positioning an area possibly containing a small target in an image by using saliency; step 2, carrying out saliency map fusion; step 3, carrying out super-separation treatment; step 4, adaptively cutting the image, and segmenting the large image by adopting a sliding window; step 5, combining repeated detection results; step 6, carrying out weighted fusion; according to the method, a set of complete processing flow from global saliency detection, candidate region screening, local super-resolution reconstruction, adaptive cutting to result fusion is formed through Step1 to Step6, and the accuracy and precision of small target detection are improved.

The invention discloses a text-region matching-based multi-tag image recognition method in a missing tag scene, which comprises the following steps of: performing cross attention operation on visual features and query vectors by using a pre-trained vision-language model as a feature extractor to generate a region-level saliency map; utilizing a multi-layer perceptron to generate query-level features, and performing aggregation operation on the visual features through the region-level saliency map to generate region-level feature representation; then, a knowledge distillation process is carried out, knowledge represented by region-level features is migrated to query-level feature representation, and based on a multi-modal comparative learning method, storage and comparative learning are carried out by utilizing a memory block visual prototype and a text prototype, so that the features of the same specific category approach each other, and the features of different specific categories are properly pulled apart; therefore, a better intra-class and inter-class relationship is established by the original model, and the accuracy of multi-label image recognition in a scene with missing labels is effectively improved.

The invention provides an electric power line defect detection method and system based on visual identification, and relates to the technical field of line detection.The method comprises the steps that firstly, a visible light and infrared image dual-light registration and differential operation technology is adopted, and a fusion feature map capable of reflecting component thermal anomaly and material difference at the same time is generated; secondly, a black box type target detection model is abandoned in a part positioning link, but line segment screening and reconstruction are carried out by combining probability Hough transform with specific prior geometric knowledge of a power line, so that dependence on a large amount of labeled data is reduced, interpretability of a positioning process is enhanced, and the positioning accuracy is improved; according to the method, accurate areas of key components such as wire insulators can be extracted in a complex background, the concept of a probability saliency map is introduced in a defect identification core link, so that a defect area is enhanced and highlighted, and then a complete defect contour is determined by adopting an adaptive threshold segmentation and area growing algorithm; accurate mapping from pixel-level features to object-level defects is realized.

The invention relates to the technical field of artificial intelligence, in particular to a class activation mapping interpretability method fusing a spatial perturbation mechanism, which comprises the following steps: a first stage: capturing contribution of each channel to prediction, and obtaining a channel attention weight of a deep learning model through a gradient method; in the second stage, disturbance analysis is introduced, and space importance weights given to image samples by the model are obtained; and a third stage: fusing the interpretation information of the two view angles through weighted integration, and generating an interpretation saliency map of the region-level granularity. According to the invention, the dual positioning capability of an interpretable technology in a channel domain and a space domain can be improved.

The invention discloses a self-adaptive diffusion image editing method and system based on concept attention, and the method comprises the following steps: constructing a paired data set; analyzing the editing instruction, and extracting a key concept; a pre-trained T5 language model is utilized to convert the key concept into text embedding, and the text embedding is mapped to an image feature space; modifying a diffusion model based on a Transform architecture, embedding a concept attention module in an attention layer of a multi-modal diffusion converter, calculating an attention score between image features and concept embedding, and generating a concept saliency map; in the denoising process, the weight of the target area is adjusted by using the concept saliency map so as to realize accurate editing. According to the method, under the condition that the global image quality is not affected, the editing precision can be improved, interference to a non-target area is reduced, and meanwhile, reinforcement learning and real-time feedback are combined, so that the model can be adaptively optimized, and an editing result better meeting the user requirement is generated.

The invention relates to the technical field of photoelectric detection and computer vision, and particularly discloses an infrared-based ground-to-air unmanned aerial vehicle detection and tracking method, which comprises the following steps: acquiring an infrared image sequence of a target airspace, dynamically adjusting the integral time and gain parameters of an infrared image by adopting a self-adaptive gain control algorithm based on a scene temperature histogram, and obtaining a target image sequence; generating an infrared image with enhanced thermal radiation characteristics; carrying out multi-level feature fusion detection on the infrared image by adopting a YOLOv5 improved multi-level global feature fusion network, and generating a saliency map containing the thermal features of the unmanned aerial vehicle rotor; according to the invention, by combining the infrared imaging technology, the improved YOLOv5 multi-level global feature fusion network and the improved BotSort algorithm, the detection precision and tracking stability of the low-altitude small unmanned aerial vehicle are effectively improved; the image quality is optimized through a self-adaptive gain control algorithm, the thermal radiation characteristic of the unmanned aerial vehicle is remarkably enhanced, and the failure problem caused by environmental interference in the traditional technology is avoided.

The invention discloses a three-primary-color depth image saliency detection system. The system comprises a feature encoder, an attention enhancement module, a cross-modal feature fusion module and a cascade correction decoder. The feature encoder is used for extracting original multi-level features of color and depth from the three-primary-color depth image; the attention enhancement module is used for enhancing the original multi-level features and carrying out space alignment processing and channel recalibration processing on the enhanced depth features; the cross-modal feature fusion module is used for performing multi-scale feature fusion processing on the depth features after space alignment and channel recalibration and the enhanced color features to obtain corresponding multi-scale fusion features; and the cascade correction decoder is used for taking the multi-scale fusion feature as a new multi-level feature of the three-primary color depth image, and decoding to generate a corresponding saliency map. The method has higher recognition and segmentation precision.

The invention discloses a multi-modal feature fusion-based light field salient target detection method. The method comprises the following steps of: performing detection by using a trained light field salient target detection model to obtain a salient map of a light field multi-modal image; according to the model, features between focus slices are enhanced in a local mode through a slice interleaving enhancement module, enhanced focus stream features are obtained, feature fusion is carried out on high-level multi-mode semantic information in a local and global mode through a high-level feature fusion module, and a fused high-level feature map is output and obtained. The low-layer multi-mode space-channel features are fused in a cross enhancement mode through low-layer cross attention, a fused low-layer feature map is output, and finally, the features are aggregated through a compact pyramid refining module and decoded into an accurate saliency map. According to the method, the light field salient target detection model is constructed, so that the problems of insufficient feature learning between slices in a focus stack and insufficient multi-modal information feature fusion in a light field are solved.

The invention discloses a traffic video saliency prediction method and system based on space-time double-branch network feature fusion. The method comprises the following steps: acquiring traffic video data to be predicted, selecting a continuous video sequence in a time branch network, selecting a single-frame video image in a space branch network, and respectively inputting the continuous video sequence and the single-frame video image into a trained significance prediction model; the method comprises the following steps: preliminarily encoding a video sequence by adopting an encoder, guiding continuous frame time sequence modeling through a saliency attention transmission mechanism, and then performing decoding operation through a deconvolution decoder to obtain a time saliency map; capturing saliency information of different receptive fields from a single-frame video image through a multi-resolution feature aggregation module which is subjected to joint sampling and layer-by-layer association to obtain a spatial saliency map; and finally, performing fusion decoding on the spatial-temporal characteristics, and calculating a final saliency map. The method can accurately predict the salient region of the traffic video in the driving scene, and has important significance for the development of the auxiliary driving technology.

The invention discloses a stratigraphic division model visualization method using confidence scores, which comprises the following steps: drawing spatial position coordinates of oil mines and screening similar oil mines to form a data set; performing abnormal value elimination and standardization processing on a logging curve in the data set, generating a two-dimensional representation fragment by using a sliding window, performing vectorization processing on a stratum label, constructing a convolutional neural network model, performing model training by using training set data, and optimizing network parameters through multiple iterations, so that the network can accurately predict the stratum label. And after training is completed, logging data fragments to be visualized are input into the trained network, and a feature map is output in a specified convolutional layer. Carrying out up-sampling on the feature map, carrying out point-by-point multiplication on the feature map and the input fragment, and inputting into the convolutional neural network with the same structure again to obtain a confidence score of the feature map; and taking the confidence score of the feature map of each channel as a weight, and performing linear weighting on the weight and the feature map of the corresponding channel to obtain a class discrimination saliency map.