51 results about "Class activation mapping" patented technology

The invention discloses an interpretability analysis and decision sharing verification method and system for a rectal cancer prognosis model, and relates to the field of medical artificial intelligence interpretability. The method comprises the following steps: carrying out gradient weighting class activation mapping analysis on a prognosis model to generate an image thermodynamic diagram; calculating the contribution degree of the multi-modal features by using an SHAP interpreter; an integrated visual interface is constructed, and patient data, model prediction and the explanation result are presented to a doctor together; the doctor performs independent risk assessment based on the interface information; finally, decisions of doctors and the model are compared, and model auxiliary efficiency is evaluated. Through a doctor-model decision sharing verification mechanism which is explained and innovated in a multi-level mode, the transparency and clinical credibility of the complex AI prognosis model are remarkably improved, the value of time sequence data in dynamic risk assessment can be verified, and clinical landing application of the AI model is powerfully promoted.

The invention relates to the technical field of fault diagnosis, in particular to a few-label self-supervised learning fault diagnosis method based on an interpretable neural network, and the method comprises the steps: collecting time domain data of a sensor of an aero-engine under different health conditions, carrying out the standardization processing, and dividing the time domain data into a pre-training set, a training set, a verification set and a test set; performing fast Fourier transform and data enhancement on the time domain data in the pre-training set to obtain frequency domain data and enhanced time domain data; constructing a pre-training framework, and performing pre-training to convergence based on the frequency domain data and the enhanced time domain data to obtain a pre-trained time encoder; constructing a fault diagnosis model based on the pre-trained time encoder, and performing iterative training to convergence by using the training set to obtain a trained fault diagnosis model; and performing fault diagnosis on the test set by using the trained fault diagnosis model, and performing visual interpretation on the diagnosis process of the fault diagnosis model by using a gradient weighting class activation mapping technology.

The invention discloses a medical image automatic identification system based on a neural network, and relates to the technical field of medical image identification. The method is used for solving the problem that early recognition of neurodegenerative diseases is difficult due to medical image and genome data splitting and poor model interpretability in the prior art. The method comprises the following steps: firstly, extracting multi-scale features of a brain structure through a three-dimensional convolutional neural network and a self-attention mechanism, calculating a multi-gene risk score based on a risk site, and encoding the score into a feature vector; secondly, using a cross attention mechanism to take gene features as query vectors, fusing the gene features with image features, and generating brain structure anomaly features under gene regulation; then, gradient weighting class activation mapping is applied to generate a visual thermodynamic diagram, and gene-image association weight weighting is combined to construct a brain region risk distribution diagram; and finally, a high-risk brain region space coordinate set is extracted through threshold segmentation, and an accurate quantification basis is provided for early recognition.

The invention discloses a fault diagnosis method, system, equipment and medium for a wind power gear box, and relates to the technical field of fault detection, and the method comprises the steps: collecting a vibration signal of the wind power gear box, and obtaining a time-frequency diagram of the vibration signal; capsule feature tensors in the time-frequency graph are obtained, a time-frequency two-dimensional network is formed in space, and corresponding query tensors are extracted from a time axis and a frequency axis of the time-frequency two-dimensional network by adopting different convolution kernels; dot product matching is carried out on the corresponding query tensors and a preset shared key value pair, attention distribution of each query tensor is obtained, and aggregation output in the directions of the time axis and the frequency axis is fused through the attention distribution; and performing class activation mapping on the aggregated output, determining a fault category of the wind power gear box, generating a class activation graph focused on the key time-frequency region through the fault category, and displaying a discrimination basis. The method can focus on a key time-frequency region with clear physical significance, and obviously improves the discrimination transparency and engineering credibility of the model.

The invention discloses a model decision interpretability method fusing integral gradient and class activation mapping, and belongs to the technical field of deep learning interpretability. Aiming at the problems of gradient noise interference, insufficient space positioning and the like existing in an existing single interpretability method, the method is optimized through five key steps: firstly, extracting a feature map set of the last convolutional layer of a deep learning model; secondly, calculating an integral gradient of the feature map to a target category based on a path integral idea; thirdly, obtaining a feature map weight through global average pooling; then, weighted summation is carried out, and an initial attribution thermodynamic diagram is generated through ReLU activation; and finally, a high-resolution thermodynamic diagram is obtained in combination with guided gradient optimization. According to the method, gradient stability of Integrating Gradients and spatial positioning advantages of Grad-CAM are fused, invariance and sensitivity axioms are realized, model adaptability is maintained, accuracy and robustness of interpretation results are remarkably improved, and the method is suitable for key fields such as signal processing and image classification.

The invention discloses a multi-modal health knowledge generation and integration system, which is applied to the technical field of fusion and application of health data, and comprises a multi-modal data acquisition and preprocessing module used for acquiring multi-modal health data and preprocessing the multi-modal health data; the multi-level fusion framework design sub-module comprises data-level fusion based on a time alignment algorithm, feature-level fusion based on a Transform cross-modal attention mechanism and decision-making-level fusion based on an ensemble learning algorithm; and the intelligent knowledge generation sub-module comprises health knowledge graph construction based on a graph neural network, personalized health risk report and intervention scheme generation based on a conditional generative adversarial network, visual image feature contribution degree based on gradient weighted class activation mapping and text data key factor analysis based on an SHAP value. According to the method, the bottleneck of a data island is fundamentally broken through, and the generation quality and the application value of health knowledge are improved.

The invention provides an improved multi-label image classification method based on multi-scale double dynamic graph convolution. The invention discloses the multi-label image classification method based on the improved multi-scale double dynamic graph convolution. The invention provides an improved multi-scale double dynamic graph convolution multi-label image classification method for solving the problems that traditional class activation mapping contains noise and the convolution effect becomes poor due to the high expansion rate of cavity convolution. The method comprises the following steps: firstly, introducing depth separable convolution to replace cavity convolution, and redesigning a combination mode of different scale features to obtain improved multi-scale features; secondly, a weight suppression mechanism is introduced to weaken class activation of irrelevant classes, and the class activation is multiplied by the improved multi-scale features to generate a new content awareness vector; and finally, inputting the content perception vector into a double-graph convolution fusion embedded network for final classification. Experimental results show that the precision of the provided model is improved on MS-COCO and VOC2007 data sets, and it is proved that the improved model has better performance.

This invention relates to a heatmap evaluation method and system based on class activation mapping and dynamic weights, comprising: acquiring crop images; inputting the crop images into a pre-trained deep learning model to output crop detection results; processing the crop detection results into a heatmap based on class activation mapping, and performing noise reduction processing on the heatmap to obtain a heatmap without background interference; performing color space conversion on the heatmap without background interference, and calculating the color difference between each pixel and a reference color, wherein the reference color includes a preset reference color and corresponding weight based on the heatmap; assigning a weight value to each pixel based on the color difference between each pixel and the reference color, and calculating an average temperature weight value; and completing a quantitative evaluation of the heatmap based on the average temperature weight value. This invention provides a new interpretive dimension and quantitative standard for the application of deep learning models in agriculture.

The invention belongs to the technical field of medical image processing and artificial intelligence, and particularly relates to a weakly supervised brain tumor segmentation method which comprises the following steps: acquiring a low-level glioma segmentation data set of a cancer genome map, and randomly dividing the data set into a training set and a test set according to patient division; based on the image data in the data set, carrying out transfer learning by using a pre-trained ResNet50 as a backbone network; a global optimization mechanism of an SZIO algorithm is utilized to perform adaptive optimization and intelligent screening on features extracted based on a traditional class activation mapping technology, and fine fusion and semantic enhancement of multi-level features are realized; and performing fine adjustment on the ResNet50 backbone network at a preset initial learning rate. According to the method, the performance breakthrough of weakly supervised brain tumor segmentation can be realized, the practicability, robustness and clinical transformation potential are relatively high, and an efficient and reliable intelligent segmentation solution is provided for the field of medical image analysis.

This invention discloses a deep learning-based weight estimation method, relating to the field of computer vision technology. By collecting side-view depth image data of a single sheep, and using sheep detection and sheep body semantic segmentation models to augment the collected data, a LiteHRNet network model is used as the backbone network of the weight estimation model to obtain semantic information at different levels from the sheep depth image data. Class activation mapping is used to visualize different head attention regions for weight estimation. Finally, a weight estimation model with small estimation error and concentrated sheep region attention is selected. This method can efficiently estimate sheep weight, thereby saving time and labor costs for livestock workers, and provides a research direction in deep learning for weight estimation of other similar animals.

The application discloses a method and system for predicting protein drug binding sites based on a multi-modal dynamic graph, comprising: obtaining amino acid sequence data and three-dimensional structure data of a protein, and generating evolutionary conservation features, structure graph topology features and sequence features respectively; inputting the features into a multi-modal fusion encoder to generate first fusion features; inputting the first fusion features into a prediction decoder to generate initial prediction probabilities; iteratively updating the structure graph topology features according to a preset three-graph update rule according to the initial prediction probabilities and residue dynamic communication scores; re-inputting the updated structure graph topology features into the multi-modal fusion encoder and the prediction decoder, and repeatedly executing until a preset iteration number is reached, to generate final prediction probabilities; and generating a residue importance heat map and outputting a prediction report based on the final prediction probabilities through a gradient weighted class activation mapping algorithm. The application realizes the cooperative optimization of prediction and graph structure, and significantly improves the accuracy of binding site prediction.

The invention discloses a car insurance claim settlement method and system based on a multi-modal large model, and the method comprises the steps: collecting the multi-modal data of a historical car insurance claim settlement case, employing a large-scale pre-training image-text large model to carry out the multi-task joint training and field adaptation fine tuning, and achieving the joint semantic understanding of an accident record text and a vehicle damage picture; the method comprises the following steps of: analyzing VIN (Vehicle Identification Number), embedding structured information, introducing a part association graph as an external knowledge constraint, and completing efficient fine adjustment of a model by adopting a hierarchical parameter freezing and low-rank adaptation technology under the condition of a small number of samples; in the reasoning stage, a structured damage analysis report, a compensation amount predicted value and confidence evaluation are output, a damage area thermodynamic diagram is generated in combination with a gradient weighting class activation mapping technology, and a visual decision basis is provided. The technical problems that traditional artificial claim checking is low in efficiency, high in cost and large in result difference, and an existing AI technology is high in data threshold, poor in field adaptability and lack of interpretability are solved.

This invention discloses an unsupervised lesion segmentation method based on large model and pseudo-label learning, belonging to the field of image recognition. First, a "Visual-Language" large model (CLIP) is used to generate high-quality classification pseudo-labels, which are then used to supervise the training of an image classification network. During this process, we further extract the class activation map (CAM) of the classification network and introduce a data augmentation strategy based on dynamic occlusion to further improve the performance of the classification network. Subsequently, we utilize these more accurate class activation maps to generate guidance information for the segmentation large model (SAM), thereby obtaining high-quality segmentation pseudo-labels. Finally, this invention proposes a self-training strategy to train and continuously optimize the segmentation network using these pseudo-labels to achieve higher segmentation accuracy and efficiency.

The invention discloses a high-spatial-resolution remote sensing image solar cell panel identification method based on deep learning. The method comprises the following steps: (1) constructing a high-spatial-resolution RGB satellite image solar cell panel data set containing various complex scenes; (2) carrying out transfer learning twice on the basis of an Inception-v3 network architecture so as to optimize a pre-training weight; (3) realizing visualization and semantic segmentation of the solar cell panel through morphological processing and threshold optimization by using a class activation mapping (CAM) technology; and (4) verifying the recognition effect through evaluation indexes (such as accuracy and F1 score). According to the method, for complex scenes such as roof angles, shadows and sundry shielding, through multi-scale feature extraction of the network and data enhancement of the data set, the precision and robustness of solar cell panel recognition are remarkably improved, and reliable data support is provided for regional solar deployment and social and economic benefit analysis.

This invention discloses a weakly supervised open-pit mining site identification method based on adaptive SAM and automatic knowledge learning. The method includes: S1, collecting images of the study area and extracting three-band images using an input spectral adapter; extracting depth features using the feature extractor of the MobileSAM model; constructing a multi-scale perception and geometric adaptation module and obtaining image features by applying N² dilated convolutional units with different dilation rates to the depth features; S2, a scene classifier using predefined prototype features to guide image features in scene classification; then, a cue generator using class activation mapping to obtain a heatmap for scene classification and selecting a set of high-confidence cue points; and a pseudo-label generator constructing triplet contrast constraints; S3, the mask decoder of the MobileSAM model combining cue information and image features to perform cue decoding and obtain the open-pit mining site identification result. This invention combines a set of high-confidence cue points with image features for cue decoding to obtain efficient and high-precision open-pit mining site identification results.

The application discloses a kind of wind power gear box fault diagnosis method, system, equipment and medium, it is related to fault detection technical field, including steps: the vibration signal of wind power gear box is collected, and the time-frequency diagram of vibration signal is obtained;Capsule feature tensor in time-frequency diagram is obtained, and a time-frequency two-dimensional network is constituted in space, and different convolution kernels are used to extract corresponding query tensor in time axis and frequency axis of time-frequency two-dimensional network;The dot product matching of corresponding query tensor and preset shared key-value pair is carried out respectively, the attention distribution of each query tensor is obtained, and the aggregation output in time axis and frequency axis direction is fused by attention distribution;Class activation mapping is carried out to aggregation output, determines the fault category of wind power gear box, and generates class activation map focusing on key time-frequency region by fault category, shows discrimination basis.The application can focus on key time-frequency region with clear physical meaning, significantly improve the discrimination transparency and engineering credibility of model.

The application discloses a kind of weak supervision target positioning methods based on multi-modal, comprising the following steps: obtaining image dataset and is divided into training dataset and test dataset, the training dataset is composed of image and classification label;Adjust the width and height of image data in image dataset, and do normalization to image;Classification label corresponding to image data is generated text data related to category by template;Conformer classification network is constructed;CLIP Text Encoder text encoder and Liner linear layer are constructed;The classification label of image is converted into text data, with the aid of the powerful image-text matching capability of CLIP, the image-text matching target added in the training process, additional supervision information is introduced for the training of network model, to alleviate the problem of class activation mapping.In addition, the application uses Conformer as the classification network of training, and uses the byproduct attention matrix of network model itself to refine class activation mapping, further improves the positioning performance of network model.

The invention discloses a pump cavitation state intelligent identification method based on multi-scale fusion DCNN, and the method comprises the steps: (1) collecting noise signals when a centrifugal pump is in different cavitation states, and segmenting the noise signals into a plurality of noise signal samples in a non-overlapping manner; (2) acquiring a wavelet time-frequency diagram of each noise signal sample, constructing a time-frequency diagram data set, and dividing the time-frequency diagram data set into a training set, a verification set and a test set; (3) constructing a time-frequency enhanced wavelet fusion multi-scale interpretable DCNN model, wherein the model comprises a time-frequency attention module, a fusion multi-scale module and a fusion class activation mapping module; (4) training the constructed model based on the training set, and storing optimal parameters of the model based on the verification set; and (5) testing the model based on the test set, and outputting the predicted cavitation state and the class activation diagram corresponding to each sample. According to the method, high classification accuracy can be obtained in the cavitation state recognition task of the centrifugal pump, and high interpretability is achieved.

The application discloses an adversarial sample visual explanation method based on a class activation map, and comprises an adversarial sample generation stage, an adversarial sample corresponding to a normal sample is generated by adding disturbance to the normal sample through an adversarial attack algorithm; a feature extraction stage, an adversarial sample feature and a normal sample feature are obtained by inputting an adversarial sample picture and a normal sample picture into a trained deep learning model for feature extraction, and sample classification is performed on the adversarial sample and the normal sample; a feature saliency map generation stage, a gradient of a classification result of the adversarial sample to the adversarial sample feature and a gradient of a classification result of the normal sample to the normal sample feature are calculated; and the gradient difference obtained by calculating the normal sample and the adversarial sample is taken as a weight, and linearly weighted fusion is performed on the adversarial sample feature to obtain a final adversarial sample feature saliency map.

The invention discloses an earphone quality test data processing method and device applying deep learning and a medium, and relates to the technical field of deep learning application. According to the method, a double-flow attention deep neural network is constructed, time domain transient features and frequency domain texture features of audio signals are extracted and fused in parallel, and deep association among different modes is captured by using a cross attention mechanism; on the basis of a mahalanobis distance between a to-be-tested sample and a golden sample in a feature space and a model prediction entropy value, a quality confidence coefficient index containing an uncertainty penalty term is constructed, and refined hierarchical management and control of earphone quality are realized; and a class activation mapping algorithm is further combined to generate a visual thermodynamic diagram, and a specific frequency band causing sound quality abnormity is accurately positioned. According to the method, the problems that a traditional testing means is insufficient in complex non-linear defect recognition capability and the result is lack of interpretation are effectively solved, and the intelligent level of earphone production line quality inspection and the defect diagnosis efficiency are improved.

The invention discloses an intelligent identification method for a service trade-off cooperative relationship of a high and cold ecosystem, and the method comprises the steps: obtaining original multi-source heterogeneous data, and carrying out the preprocessing of the data, and obtaining a spatio-temporal data set; constructing a meta training task set based on the spatio-temporal data set so as to train a meta learner, generating corresponding optimal model parameters through the trained meta learner according to a future climate-land utilization scene, inputting the optimal model parameters into a corresponding model for chain simulation, and outputting future multi-time-sequence ecosystem service supply spatial distribution data; according to the space-time diagram convolution network model, obtaining a relation intensity space distribution diagram and further identifying a trade-off cooperative relation, generating a space activation thermodynamic diagram through a gradient weighting class activation mapping technology, and after the space activation thermodynamic diagram is fused with the relation intensity space distribution diagram, determining a key area space distribution diagram according to a quantitative decision rule. And performing rule matching in combination with the ecological management knowledge base, and determining a differentiated ecological management scheme. According to the invention, the recognition accuracy and efficiency can be improved.

The embodiment of the application relates to the technical field of wireless communication, in particular to a sample attack defense effect explainable driving electromagnetic signal sensing method, which comprises the following steps: acquiring IQ data of an original electromagnetic signal, and generating normal samples based on the IQ data; adding attacks to the IQ data to obtain IQ data after attacks, and generating adversarial samples based on the IQ data after attacks; based on the adversarial samples, checking feature representation inside a neural network from the perspective of model failure, explaining attack effects of samples in an electromagnetic signal sensing process, and verifying success of adversarial sample generation; based on a deep neural network model, performing adversarial defense through adversarial training, and verifying adversarial defense verification using adversarial training from an explainable perspective by adopting a gradient-based class activation mapping method. The sample attack defense effect explainable driving electromagnetic signal sensing method provided by the application can realize intelligent sensing of electromagnetic signal samples with explainable attack defense effects.

The present disclosure provides a data enhancement method, a model training method and an image processing method and device, relates to the technical field of artificial intelligence, in particular to the computer vision technology. The specific implementation scheme is: obtaining a class activation mapping corresponding to a training sample for data enhancement, wherein the class activation mapping is generated based on a sample image of the training sample and a target image processing model trained by the training sample; generating an attention mask matching the size of the sample image based on the class activation mapping corresponding to the training sample; and performing data enhancement on the pre-acquired sample image based on the pre-acquired noise matrix and the attention mask to generate a new training sample corresponding to the training sample. Thus, the adaptability of the data enhancement method to different models can be improved, thereby helping to improve the training effect of the model.

The application discloses an agricultural seed screening method and system based on data analysis, relates to the technical field of data processing, obtains a transmission mode hyperspectral image of a seed to be tested, obtains an optimal contrast reference image based on an OTSU method and generates a global binary mask, obtains an effective connected region based on the global binary mask, performs full-waveband data cutting on the transmission mode hyperspectral image, generates a region of interest, determines a key characteristic wavelength through a one-dimensional deep full convolution neural network model and a class activation mapping algorithm, calculates geometric morphological feature data of the seed to be tested in the region of interest, obtains an optimal spectral feature vector of the seed to be tested based on the key characteristic wavelength, constructs an original geometric feature vector and an original spectral feature vector, performs normalization processing, generates a graph fusion feature vector, constructs a dynamic classification model, and if it is determined that the seed to be tested falls into an unqualified area or a risk buffer area, drives a pneumatic nozzle to realize physical separation.

The application discloses an interpretable analysis and decision sharing verification method and system for a colorectal cancer prognosis model, and relates to the field of medical artificial intelligence interpretability. The method comprises the following steps: performing gradient weighted class activation mapping analysis on the prognosis model to generate an image heat map; calculating the contribution degree of multi-modal features by using a SHAP interpreter; constructing an integrated visualization interface to present the patient data, model prediction and the above-mentioned explanation results to doctors; the doctors perform independent risk assessment based on the interface information; and finally, the decisions of the doctors and the model are compared to evaluate the auxiliary performance of the model. Through multi-level explanation and innovative doctor-model decision sharing verification mechanism, the transparency and clinical credibility of the complex AI prognosis model are significantly improved, the value of time series data in dynamic risk assessment can be verified, and the clinical landing application of the AI model is effectively promoted.

The present application relates to a target detection network evaluation method based on quantitative evaluation and explainability method. The present application combines quantitative evaluation and explainability method, which can more comprehensively and accurately evaluate the performance and decision basis of the target detection network. The present application uses multiple quantitative evaluation indicators (accuracy, recall rate, average precision, FPS) to comprehensively evaluate the target detection network. An explainability method based on perturbation and class activation mapping is proposed, which can help users understand the decision-making process of the target detection network. A visualization method based on gradient descent method and regularization technique is proposed, which can help users understand the semantics contained in the target detection network. The explainability method proposed in the present application can intuitively show the internal operation process and knowledge of the target detection network, thereby enhancing the explainability of the model and improving the credibility of the model results. Experimental results show that the present application can effectively evaluate the performance of the target detection network, improve the explainability of the model, and can be used for the design, evaluation and improvement of the target detection network.

The present disclosure provides a method and device for training an image processing model and image processing, and relates to the technical field of artificial intelligence, in particular to the technical field of deep learning and computer vision. The specific implementation scheme is as follows: inputting a sample image of a pre-acquired training sample into a pre-acquired initial image processing model to obtain a class activation mapping corresponding to the sample image and an attention heat map corresponding to a target feature extraction layer, wherein the training sample comprises the sample image and corresponding label information; training the initial image processing model based on minimizing the difference between the label information and the output of a full connection layer in the initial image processing model and an attention difference to obtain an image processing model, wherein the attention difference is determined based on the difference between the class activation mapping corresponding to the sample image and the attention heat map corresponding to the target feature extraction layer. Thus, guided learning of the attention weight of the attention mechanism is realized.

The application discloses a kind of based on class activation area offset metric's continuous learning performance evaluation method, belong to the class incremental continuous learning field of depth neural network model.This application carries out image processing task, and depth neural network model needs to extract and focus on the object to be processed in image, to complete subsequent downstream task;In the continuous learning scene, the catastrophic forgetting of model in the learning of subsequent task can cause its extraction and attention ability to lose target in previous task.This scheme proposes to use Grad-cam class activation mapping spectrum to measure the effectiveness of continuous learning algorithm to curb catastrophic forgetting in different task stages, mainly gives the quantitative attention area stability evaluation index, and forms the deviation and correlation index in combination with classification accuracy, finally, DV and RL are integrated as DR evaluation index to measure the overall performance of continuous learning algorithm.

The application discloses an ultrasound carotid artery intima-media segmentation method based on class activation mapping feature fusion, and the method comprises the following steps: acquiring a to-be-detected CAUS image and inputting the to-be-detected CAUS image into a pre-constructed segmentation model; performing feature extraction on the to-be-detected CAUS image based on a feature extraction network to obtain feature relationship image information; generating an activation map and activating a global object region according to the feature relationship image information based on a region activation iterative generation module; generating local features and global features according to the global object region based on a feature generation module; and fusing the global features and the local features based on a feature fusion module to obtain a final segmentation map. Through the use of the application, the carotid artery intima-media boundary can be automatically, quickly and accurately segmented. The application can be widely applied to the field of image segmentation as an ultrasound carotid artery intima-media segmentation method based on class activation mapping feature fusion.

The invention discloses a weak supervision open stope identification method based on adaptive SAM and automatic knowledge learning, and the method comprises the steps: S1, collecting an image of a research region, inputting the image into a spectrum adapter, extracting a three-waveband image, and extracting depth features through a feature extractor of a MobileSAM model; constructing a multi-scale perception and geometric self-adaption module, and processing the depth features through N2 hole convolution units with different expansion rates to obtain image features; s2, a scene classifier uses predefined prototype features to guide image features to realize scene classification; then, a prompt generator classifies the scenes by utilizing a class activation mapping method to obtain a thermal activation graph and screens a high-confidence point prompt set, and a pseudo tag generator constructs a triple comparison constraint; and S3, a mask decoder of the MobileSAM model carries out prompt decoding in combination with the prompt information and the image features to obtain an open stope identification result. According to the method, prompt decoding is performed in combination with the high-confidence point prompt set and the image features, so that an efficient and high-precision open stope recognition result is obtained.