3695 results about "Detection performance" patented technology

The invention relates to a microchip appearance defect detection method based on a convolutional neural network. The microchip appearance defect detection method comprises the following steps: acquiring a high-resolution microscopic image and carrying out adaptive brightness equalization processing; labeling defect features in the pre-processed image by adopting a layered labeling strategy, and dividing defects into a surface deformation class and a material anomaly class by coarse-grained classification labeling; a double-branch lightweight convolutional neural network model is constructed based on the annotation data, a first branch extracts global semantic features, and a second branch extracts local detail features. In the model training, a defect sensitivity loss function is adopted for optimization, and defect area attention, edge preservation and category balance loss items are included, so that the small-size defect detection capability and the boundary positioning precision are improved, and the problem of sample imbalance is solved. And finally, a detection result including defect types, position coordinates and confidence scores is generated by inputting a to-be-detected preprocessed microscopic image, and the defect detection performance with high precision and strong pertinence is achieved.

The present invention relates to the technical field of intelligent remote-sensing-image interpretation, and specifically provides a texture-aware and boundary-aware target detection method and system for a weak and small target in a remote sensing image. The method comprises: inputting a remote sensing image into a feature extraction network to extract a basic feature, and then extracting a texture-aware feature; inputting the remote sensing image into a boundary map extraction module to extract a binary boundary map, and then extracting a boundary-aware feature; fusing the boundary-aware feature with the basic feature by means of a boundary-guided feature module, so as to obtain a boundary-guided feature; and decoupling the texture-aware feature and the boundary-guided feature and outputting same, inputting the texture-aware feature and the boundary-guided feature into a task-decoupling RCNN for double-branch decoupling prediction, so as to obtain a classification and positioning result, and completing texture-aware and boundary-aware target detection. By means of the method and system in the present invention, information that is hard for remote-sensing weak and small targets to show can be mined, thereby further improving the expression of the weak and small targets, and thus improving the detection performance regarding the weak and small targets.

The invention discloses an infrared ship detection method based on an improved RT-DETR algorithm, and aims to improve the detection precision and efficiency of a ship target in an infrared image. By introducing a multi-layer dynamic feature conversion module (MDST), the diversity of feature extraction is enhanced; effective fusion of multi-scale features is realized by using a high-level screening feature fusion pyramid network (HS-FPN); an original RT-DETR backbone network is replaced by a PPA and C2f structure, so that the problem of information loss in a down-sampling process is solved; and the CA module is used for enhancing the feature selection and fusion effect. Experimental results show that the provided method significantly improves the detection performance on an infrared marine ship data set, is suitable for resource-constrained equipment such as unmanned aerial vehicles and smart phones, and has a wide application prospect.

The invention relates to a visible light and infrared multi-mode target detection method, in particular to a target detection method based on infrared and visible light feature enhancement and fusion, which comprises the following steps of: firstly, constructing a double-branch feature encoder suitable for infrared and visible light double input, and respectively extracting double-mode features through a double-branch encoding network; secondly, in order to better utilize the bimodal features to carry out target detection in a complex scene, a DBE module is provided, and the internal feature attention is utilized to enhance information interaction between modals. In addition, a cross attention mechanism is introduced to further improve the expression ability of the network to complementarity information, so that the detection performance of the model is effectively improved. Experimental results show that compared with an existing method, the method disclosed by the invention has excellent performance on an SYUGV data set. For complex environments such as single-mode information loss caused by mutual shielding influence of targets and local transition exposure, the method disclosed by the invention still keeps good detection performance and has relatively high robustness.

The invention discloses an event and RGB image fused SNN target tracking method and system, and the method comprises the steps: converting an event flow from an event camera into an event frame image through a representation method based on time stacking, and enabling the event frame image to be aligned with an RGB frame image from an RGB camera in time and space; performing target detection on the event frame image # imgabs0 # and the RGB frame image # imgabs1 # after alignment by using a spiking neural network SNN, wherein the spiking neural network SNN comprises a feature extraction module, a feature fusion module and a detection head; and learning the similarity of the targets by using the anchor frame extracted by the spiking neural network SNN and the fusion feature map # imgabs2 # to perform multi-target tracking. The invention aims to realize fusion of the fusion event and the RGB image so as to ensure high precision and high efficiency of multi-target tracking, and keep stable detection performance and tracking performance in static and high-motion scenes.

The invention provides a multi-feature fusion rumor detection method, system and device based on knowledge distillation, and mainly solves the problems that an existing model is high in calculation overhead, insufficient in feature fusion and insufficient in emotion utilization. The method comprises the steps of firstly obtaining multi-dimensional data such as social media original texts and comments; extracting deep semantic representation by using a pre-training model, and analyzing comment emotion features in combination with a hybrid neural network; then, features such as semantics, emotions, emoticons and populations are input into a hierarchical gating interactive fusion network (GIFN), and weights are dynamically adjusted to achieve effective fusion of multi-granularity features; in order to reduce complexity, a knowledge distillation framework is designed: a deep GIFN is used as a teacher network to generate a soft label, and a lightweight student network (LSTM) is guided to perform training. According to the trained student model, the parameter quantity is remarkably reduced, meanwhile, good detection performance is kept, the student model can be conveniently deployed in an actual content auditing system or edge equipment, and social content rumors can be efficiently recognized and judged.

The invention provides a PCBA surface defect detection method and system based on image recognition, and the method comprises the steps: collecting and constructing a multi-class PCBA overall defect image data set, and carrying out the preprocessing; constructing a pre-training neural network model, and classifying the preprocessed PCBA overall defect image data set; constructing a local target identification method based on a YOLOX network according to an overall defect classification result; and carrying out local defect detection on the PCBA surface based on a local target identification method. According to the method, the overall classification and local detection strategies are comprehensively applied to reduce the burden of the model, and the detection precision and speed are improved. And an attention mechanism is introduced to improve the performance and generalization ability of the model. Data enhancement, consistency processing, classification labeling and the like are carried out on an image data set, and the defect detection effect of the model is improved. And multiple optimization strategies such as structure improvement, loss function improvement and pruning lightweight are adopted for the network model, so that the image target detection performance is effectively improved.

The invention provides a thin film surface defect detection method and system, and relates to the technical field of defect detection.According to the thin film surface defect detection method and system, an intelligent secondary verification link is constructed by introducing a defect confidence evaluation mechanism based on form and energy distribution, so that the detection performance is fundamentally improved; according to the mechanism, real physical defects with regular forms and concentrated energy and pseudo defects caused by electromagnetic interference, instantaneous film wrinkles and the like can be accurately distinguished, and the problem of high false alarm rate caused by dependence on single signal strength in the prior art is effectively solved while the high detection rate of low-contrast defects is reserved; besides, the judgment model based on physical characteristics has natural robustness for background noise generated in high-speed motion, and an adjustable confidence threshold value endows the system with extremely high practical flexibility, so that the system can adapt to complex and changeable industrial environments and different quality control standards, and the method is suitable for large-scale popularization and application. And the accuracy, the reliability and the intelligent level of the whole detection system are obviously enhanced.

The invention discloses an unmanned aerial vehicle aerial image target detection method based on PSO-DETR, and belongs to the technical field of unmanned aerial vehicle aerial image target detection. Firstly, a parallel patch perception attention feature extraction module is constructed, and an efficient multi-branch backbone network C3KCSPnet is designed by fusing a CSPDarknet53 structure; according to the network, gradient flow is improved through deep optimization, and the capturing capacity of high-level semantic information is enhanced. And secondly, an enhanced channel offset hybrid operator is provided, the dependency relationship between channels is enhanced through a channel shuffling mechanism, and cross-channel interaction of local space information is realized in combination with channel offset operation, so that the feature recovery quality and fusion efficiency in an up-sampling stage are improved, and the problem of missed detection of a shielded target is further relieved. And finally, a re-parameterization hierarchical aggregation network is designed, effective integration of shallow details and deep semantics is realized through an efficient hierarchical fusion mechanism on the premise of ensuring controllable calculation complexity, and the detection performance of the small target is further enhanced.

The invention discloses an underwater target detection method based on vision-language model knowledge distillation. Along with continuous maturity of a vision-language large model, the powerful visual feature extraction capability and text alignment capability are widely applied to the field of deep learning. The method comprises the following steps: firstly, collecting and fusing a plurality of public underwater data sets, and cleaning and preprocessing the data to construct a rich and high-quality training data set; secondly, a vision-language model is introduced, in a large model fine tuning stage, feature extraction and fine-grained fine tuning are carried out through image and text alignment, association between the image and the text is effectively captured, and therefore more accurate feature representation is generated; and finally, in a knowledge distillation stage, the method adopts a teacher network and a student network to carry out feature migration. The teacher network obtains fine features from the backbone network after fine tuning, and transmits the knowledge to the student network through a mean square error loss function, thereby improving the generalization ability and detection performance of the student network, and finally achieving the robust detection of an underwater target. According to the method, the vision-language model and the knowledge distillation technology are fully utilized, the underwater target features are enhanced, the model can learn the robust target features, and the detection robustness is improved.

The invention discloses a cross-domain face anti-counterfeiting detection method and device based on multi-modal text enhancement, and relates to the technical field of network information security, and the method comprises the steps: inputting two types of description texts into a pre-trained text encoder, and extracting text type features representing reality / deception, inputting the image into a pre-trained visual encoder to extract visual features; a trainable text prompt is added to each layer of the text encoder, a trainable visual prompt is added to each layer of the visual encoder, and the visual prompt of each layer of the visual encoder is obtained by converting the text prompt of the current layer through a full connection layer; and embedding the PFT module and the TIM module into a middle layer of each layer of a text encoder and a visual encoder to realize feature interaction fusion, obtaining cosine similarity and masks between text category features and visual features, and performing face true and false classification. Based on the PFT module and the TIM module, modal feature interaction is completed in the feature extraction process at the same time, and the cross-domain detection performance is improved.

The invention relates to the technical field of semiconductor manufacturing, in particular to a semiconductor defect detection method and system based on deep learning. The method comprises the following steps: step 1, multiband cooperative imaging and dynamic scanning control; 2, performing multi-scale feature fusion and defect identification; 3, dynamic threshold judgment and multi-scale feature fusion network optimization: dynamically adjusting a judgment threshold according to probability distribution characteristics of a current batch defect thermodynamic diagram, applying an offset to the threshold in combination with a wafer process type, and determining the offset according to a balance relationship between a false drop rate and an omission rate in historical data; critical samples and misjudgment samples with classification confidence close to a threshold value in historical detection are periodically screened, incremental learning is performed on the last layer of the multi-scale feature fusion network, and upstream network weight is frozen to prevent feature drift. Through fine image processing, multi-band information fusion, dynamic threshold adjustment, incremental learning and other mechanisms, the defect detection efficiency and accuracy in the semiconductor manufacturing process can be effectively improved, and the detection performance is continuously optimized.

The invention relates to the technical field of nondestructive testing, in particular to a multi-modal fusion metal microcrack ultrasonic detection system and method, and the system comprises an ultrasonic signal acquisition module, a signal preprocessing module, a feature alignment network module, an image segmentation module, a time-frequency analysis module and a segmentation result fusion module. According to the system, three modes of electromagnetic ultrasound, laser ultrasound and phased array ultrasound are utilized to detect a metal sample, and real-time nondestructive signal data acquisition and pretreatment are realized. The feature alignment network aligns and fuses the three modal signal features, and the U-Net segmentation neural network performs submillimeter crack segmentation on the fused features. And the time-frequency analysis module extracts signal frequency and amplitude information, and evaluates the breaking strength in combination with the crack label. Finally, a segmentation result fusion module integrates crack labels and time sequence features, and whether cracks exist or not is classified through a weighted convolutional neural network. According to the system, the detection performance, the practicability and the adaptability are remarkably improved, and a new scheme is provided for precise detection of metal microcracks.

The invention discloses a semi-supervised multi-temporal satellite image time-varying information extraction method, and belongs to the technical field of remote sensing image processing. The semantic change detection performance of the model and the detection precision of complex shape change ground objects are improved. The method comprises the following steps: constructing a semantic change detection model, carrying out full-supervised training on the semantic change detection model by using a binary change detection supervised loss function and a semantic segmentation supervised loss function by using a small amount of labeled dual-temporal remote sensing images to obtain an initial model, and obtaining a semantic change detection prediction result of each pair of images; using a pseudo label optimization strategy to optimize the semantic change detection prediction result of each pair of images; combining the obtained pseudo label data with high confidence and a small amount of labeled dual-temporal remote sensing images into a new training set, using the new training set to perform semi-supervised training on the initial model in a semantic change detection model, and using a consistency regularization combination loss function to perform supervised training to obtain a new model; and until a preset number of iterations is reached.

The invention discloses an image detection method based on an improved YOLO algorithm. The method comprises the following steps: collecting a nuclear magnetic resonance image with a focus; a target detection network model is constructed: basic framework improvement is carried out based on YOLOv8, an improved backbone network comprises a C2f module, a convolution module and an improved multichannel spatial pyramid pooling module SPPMC, and the image feature extraction capability is enhanced; the improved neck network comprises an up-sampling layer, a splicing layer, a C2f module, a convolution layer and an embedded attention mechanism UECA module, multi-scale feature map fusion is carried out, and the feature fusion efficiency is improved; the improved header network adopts an improved bounding box regression loss function L (iS-IoU), the convergence speed of the model is increased, and the positioning precision is improved; inputting a training image set into the target detection network model for training; and performing image focus detection by using the trained target detection network model. The method has the characteristic of effectively improving the robustness and the detection performance of the target detection model.

The invention relates to the technical field of data processing, in particular to a road disease detection method and system based on an autonomous agent. The detection method comprises the following steps: constructing and deploying an autonomous agent, controlling a ground penetrating radar unit GPR to obtain road internal disease data based on a large language model of the autonomous agent, and obtaining road apparent disease data through an image acquisition unit; the autonomous agent obtains and preprocesses road internal disease data and road apparent disease data, then calls a pre-trained semi-supervised detection algorithm for processing by using a plurality of sub-agents, and performs multi-agent decision fusion on decision results of the sub-agents through weighting to determine an internal disease detection result. And types and positions of the road internal diseases and the road apparent diseases are obtained. Through cooperative application of the main learning intelligent agent technology and road disease detection, the detection performance is improved.

The invention provides a vision-based maritime work equipment monitoring method, which comprises the following steps of: acquiring and preprocessing a maritime work site image, and performing image segmentation by using an SAM model to automatically generate a high-quality target mask; and in combination with a text prompt input by a user, the Grounding DINO model automatically detects the maritime work equipment in the image and generates a detection frame, and the detection frame is combined with a segmentation mask of the SAM model to refine a detection result. A Grouping DINO model is finely adjusted on an SAM automatic labeling data set, the ability of the Grouping DINO model to adapt to the marine environment is improved, and the detection performance is continuously improved through the loop optimization process. According to the method, excellent performance is shown on a ShipDataset data set, the average precision (AP) of the method exceeds that of a traditional model, particularly on maritime work equipment detection, high robustness and precision are shown, and powerful technical support is provided for safety and efficiency of offshore operation.

The invention discloses a multi-scale context enhancement small target detection method based on an improved RT-DETR (Reverse Transcription DET Rate). The method comprises the following steps: preprocessing unmanned aerial vehicle image data, and then constructing an improved RT-DETR model; the core is that a CSP-GFCG feature extraction module is used for modulating a feature map through frequency domain transform (DFT / IDFT) and a learnable global filter by using GFNet to realize global context modeling; and then the processed features are input into a ConvGLU module, and local features are enhanced in combination with depth separable convolution and a gating linear unit. GFNet and ConvGLU cooperate with each other, and challenge is effectively reserved for scale change and details in small target detection. The method aims at optimizing a feature extraction mechanism, reducing redundancy and improving the detection performance of a small target under a complex background. Meanwhile, the calculation efficiency is improved, the resource consumption is reduced, and the problem of missing detection of small targets is effectively solved.

The invention provides an aluminum film sealing defect real-time detection method and system based on multi-algorithm fusion, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: triggering an industrial camera at a detection station to collect an original image of a pesticide aluminum film sealing on a conveyor belt; performing adaptive equalization operation on the original image through pixel brightness distribution data, eliminating light fluctuation and surface reflection interference, and outputting a standardized image; three types of defect detection are synchronously executed based on the standardized image, a dynamic threshold segmentation algorithm is combined with local area brightness analysis to detect edge damage, a contour extraction algorithm is adopted to calculate bottleneck center offset to recognize seal offset, wrinkle defects are recognized based on a surface texture feature analysis algorithm, and a primary detection result is output. The aluminum film sealing defect detection method is based on multi-algorithm fusion, has strong anti-interference capability, real-time detection performance and data traceability, and provides an efficient and reliable automatic solution for aluminum film sealing quality management and control.

The invention relates to a multi-modal sparse fusion three-dimensional target detection method in an unstructured environment, and the method is based on a complexity perception candidate region dynamic sampling strategy, and through the cross-domain self-compensation of point cloud geometric features and image spectral features, and the fusion of multi-view sparse features, the detection of a multi-modal sparse fusion three-dimensional target is realized. The perception capability and the recognition precision of the obstacle area, and the remote dynamic target detection performance and the real-time performance are improved; an anti-deformation convolution group and a terrain gradient perception attention module are introduced into a ParScaleNet image backbone network, so that the extraction of edge features of a complex scene is enhanced, and the cross-scale characterization capability of the features is improved; according to the method, a ContraSpaceOpt module with semantic maintenance capability is deployed behind a Transform, excessive smoothness of obstacle features is inhibited through class comparison loss functions and feature space anisotropy constraints, the space separability of obstacles is maintained, and the problems of point cloud fracture and texture misalignment caused by the obstacles are effectively solved.

The invention belongs to a target detection technology in the field of computer vision, and particularly relates to an X-ray security check image hazardous article detection system based on improved YOLOv11. Firstly, an X-ray security check image data set is divided into a training set and a test set according to the proportion of 6: 4, the system is based on a YOLOv11 architecture, a space and channel cooperative attention mechanism (SCSA) is introduced into a backbone network structure, the SCSA comprises two parts, namely sharable multi-semantic space attention (SMSA) and progressive channel self-attention (PCSA), and the SCSA is used as a main network of the system. The objective of the invention is to improve the detection performance of hazardous articles in an X-ray security inspection image, effectively enhance the understanding ability of a model for complex scenes, and improve the detection precision and speed of small targets, shielded objects and low-contrast hazardous articles. According to the method, the YOLOv11 algorithm is improved by combining the SCSA module, the detection precision and speed of the dangerous goods in the X-ray security check image are remarkably improved, and powerful technical support is provided for improving public safety and optimizing the security check process.

The invention relates to a training method and device of a safety equipment detection model, equipment and a storage medium. The method comprises the following steps: acquiring a sample image, inputting the sample image into a to-be-trained safety equipment detection model, performing preliminary feature extraction by a trunk module in the model to obtain initial image features, inputting the initial image features into a plurality of depth separable convolution modules with preset self-attention mechanisms in the model, and obtaining a plurality of depth separable convolution modules with preset self-attention mechanisms; a feature map is obtained through a depth separable convolution module, a corresponding attention weight is obtained through a self-attention mechanism, a weighted feature map is obtained through the information, then the weighted feature map is input into a feature fusion module of a model for self-adaptive fusion, a fused feature map is obtained, and a prediction detection result is obtained according to the fused feature map. And finally, according to the difference between the results, performing model training on the to-be-trained safety equipment detection model to obtain a trained safety equipment detection model. By adopting the method, the detection precision and the detection performance of the model can be improved.

The invention discloses a network intrusion detection method and system based on edge attention learning, and a storage medium, and the method comprises the steps: converting an original network flow into a network flow diagram, and constructing a training diagram and a test diagram under the condition that a coarse-grained label and a fine-grained label are reserved; edge embedding representation is obtained through edge feature reservation, adaptive weight distribution and multi-layer feature extraction of the training graph and the test graph; based on the edge embedding representation, performing coarse-grained detection to identify a basic attack category, and performing fine-grained classification by using multi-scale feature fusion related to global graph attributes; and adversarial training: through initializing adversarial disturbance and optimizing disturbance based on loss function gradient iteration, superposing final disturbance into the training graph, and based on loss function back propagation updating, obtaining a trained network intrusion detection model. The method provided by the invention can effectively capture the depth characteristics of the key attack and maintain the stable detection performance in the confrontation environment.

The invention provides an efficient and accurate small target detection method aiming at the problems of low detection precision, high false detection rate and poor real-time efficiency of small target objects in the field of automatic driving. According to the method, a YOLOv8 network is used as a baseline model, a dynamic enhancement convolution module is introduced into a backbone feature extraction network, and more detailed target detail information is extracted. And an adaptive weighted receptive field fusion module is adopted, so that image feature enhancement of diversified receptive fields is realized, and the richness and adaptability of feature representation are improved. And a large kernel channel space attention module is utilized to highlight a small target area and suppress background noise, so that the distinguishing capability and the detection performance of the whole model are remarkably improved. According to the technical scheme, the precision of small target detection is effectively improved, the omission ratio and the false detection ratio are reduced, and the perception capability and the safety of the automatic driving system in a complex traffic environment are enhanced.

The invention proposes an improved YOLOv8 coal gangue detection method, and the method comprises the steps: obtaining a coal gangue image, carrying out the preprocessing of the coal gangue image, and dividing the coal gangue image into a training set and a test set; the method comprises the steps that an OSM-YOLOv8n model is constructed, the OSM-YOLOv8n model comprises a backbone network, a neck network and a head network, the backbone network and the neck network construct a C2fORPA module through a block linearization and block extrusion method, a SegNextAttention module is constructed at the tail of the backbone network, and an MPDIOU loss function is designed in the head network; using the training set to train the constructed OSM-YOLOv8n model, using the test set to evaluate the performance of the trained model and adjust the parameter selection model, and finally obtaining the trained model; and acquiring a coal gangue image needing to be processed, and inputting the coal gangue image into the trained OSM-YOLOv8n model to obtain a classification detection result. The method reduces the complexity of the model, reduces the training cost, enhances the understanding of the model for the image, and improves the detection performance.

According to the radar moving target detection method based on small sample transfer learning and the attention mechanism, a small amount of actual radar echoes and high-fidelity scene simulation data are used as a data training set, and direct detection of a moving target under a strong clutter background can be realized. An attention mechanism is introduced by utilizing the point / line distribution characteristics of a moving target and a scene clutter in a space-time two-dimensional plane, the clutter sorting and recognition capability of the network can be ensured while the network training speed is improved, meanwhile, a small sample migration twin network is further combined, a small amount of actually measured data can be utilized to correct a target detection network, and the target detection accuracy is improved. The adaptability of the network to actual working conditions is improved, and the generalization ability is improved. According to airborne flight calibration test data analysis, under the condition of a low signal-clutter-noise ratio, compared with a traditional STAP + CFAR technology, the method has higher detection performance and lower false alarm rate. Meanwhile, based on network pre-training and a small sample transfer learning mechanism, the method has practicability in the aspect of early warning radar real-time processing and has high engineering application value.

The invention relates to the technical field of intelligent traffic supervision, and discloses a vehicle tarpaulin coverage real-time detection method based on multi-modal feature fusion, and the method comprises the following steps: S1, constructing a bimodal input data stream; s2, the bimodal images are preprocessed respectively; s3, extracting features; s4, dynamically fusing the bimodal features through an adaptive feature fusion module; s5, inputting the mixed feature map into a lightweight convolution detection network, and outputting a segmentation mask; s6, motion trail compensation is carried out on the dynamic vehicle; by designing a multi-modal adaptive feature fusion mechanism, the robust detection performance in a complex environment is improved: the fusion weight is dynamically adjusted based on the environment illumination and the temperature gradient, so that the system automatically strengthens effective modal features under extreme conditions such as strong light, night, rain and fog and the like; by introducing a dynamic motion compensation framework, the problem of motion fuzzy interference of a running vehicle is effectively solved, and the boundary precision of dynamic vehicle tarpaulin coverage detection is improved in a breakthrough manner.

The invention discloses a small target traffic sign recognition method based on improved YOLOv8, and the method comprises the steps: constructing a data set, selecting sample pictures from the data set, carrying out the labeling and data enhancement, and dividing the data set into a training set and a test set; the YOLOv8 model is improved, and a multi-scale attention mechanism EMA is embedded in a backbone network of the YOLOv8 model; an up-sampling operator in a neck network of the YOLOv8 model is replaced by a DySample up-sampling operator; replacing an original loss function CIoU of the YOLOv8 model with a WIoUv3 loss function; and inputting the training set into the improved YOLOv8 model for training, after training is completed, detecting the detection performance of the improved YOLOv8 model by using the test set to obtain an identification result, and evaluating the performance of the improved YOLOv8 model by using various indexes. According to the small target traffic sign identification method based on the improved YOLOv8, the identification precision and the identification speed of the small target traffic sign can be effectively improved.

The invention relates to a transformer voiceprint anomaly detection method based on a generative adversarial network, and the method comprises the steps: obtaining voiceprint data, and carrying out the preprocessing of the voiceprint data, and obtaining the preprocessed spectrum data; constructing an abnormal voiceprint detection model; inputting the preprocessed spectrum data into an abnormal voiceprint detection model for training; and obtaining voiceprint data to be detected and preprocessing the voiceprint data to be detected, inputting the preprocessed voiceprint data to be detected into the trained abnormal voiceprint detection model, and outputting a detection result. According to the method, unbalanced distribution of normal voiceprint data and abnormal voiceprint data can be balanced, the generalization ability of the model is enhanced, and the model can keep high detection performance when processing a small amount of abnormal data; according to the method, the distinguishing capability of the abnormal voiceprint is enhanced, the abnormal voiceprint can be effectively recognized in a complex noise environment, the influence of noise and interference factors is reduced, and the detection accuracy is improved; fine-grained features in the voiceprint signals can be captured, extraction of the features is further optimized, and therefore abnormal voiceprints can be recognized more accurately.

The invention discloses a road traffic sign target detection method based on YOLOv8, and relates to the field of road target detection, in the method, a CDFF-YOLO model is constructed, and an MPA module is embedded in a feature fusion network of the model to enhance the feature fusion capability of C2f; an MSF module is constructed, GSConv and CARAFE up-sampling operators are embedded to realize a multi-dimensional sequence information fusion effect, and the detection precision of the model is improved while the parameter quantity and the calculation quantity of the model are reduced as much as possible. A DFF module is constructed, two-dimensional Fourier transform and inverse transform methods are adopted to enable feature information to be enhanced in a frequency domain and a space domain at the same time, and the detection performance of a network model for shielded small targets and small targets under uneven illumination is effectively improved. The three modules not only enhance the detection effect of the small target model, but also enable the network to stably cope with the detection scene of the small target. According to the method, the detection performance of the small road traffic sign target is excellent, the processing speed is high, and deployment of mobile terminal equipment is facilitated.