2789 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 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 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 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 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 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 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 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.

The invention belongs to the technical field of AI vision, and particularly relates to a universal six-degree-of-freedom grabbing pose detection method and system for a robot. According to the detection method, the point cloud feature extraction process is optimized by introducing an attention mechanism, so that the prediction precision of the detection head on the six-degree-of-freedom grabbing pose is improved. The method comprises the following steps: firstly, carrying out serialization processing on unstructured, sparse and non-uniform point cloud data generated from an RGB-D image, and then designing a multi-encoder-decoder cascaded backbone network based on a Transform architecture so as to effectively extract rich features of the point cloud. On the basis, a network module special for six-degree-of-freedom grabbing pose detection is designed, and the predicted six-degree-of-freedom grabbing pose is optimized in combination with antipodal grabbing constraints, so that it is ensured that the six-degree-of-freedom grabbing pose conforms to actual physical rules. Through verification of a grabbing experiment on an actual Franka robot, the general grabbing pose detection performance of the method on any object in actual application is further proved.

The invention discloses an industrial Internet of Things time sequence self-supervision anomaly detection method and a monitoring and early warning system, and relates to the field of industrial Internet of Things, and the method comprises the steps: S1, constructing an anomaly detection model, and S2, obtaining a training data set; s3, training and optimizing an anomaly detection model; s4, acquiring to-be-detected data in real time; s5, performing anomaly detection analysis on the to-be-detected data, and outputting an anomaly detection result; through a time sequence and relation learning module, a dynamic graph topological structure learning module and an enhancement module, internal characteristics of a time sequence in a time domain and a space domain are deeply mined. The time sequence and relation learning module comprehensively captures a multi-scale time pattern, and the dynamic graph topological structure learning module eliminates dependence on a predefined graph structure; the enhancement module enhances the invariant representation under noise, and improves the recognition capability of the model to a normal mode; through wide experiments, the advancement of the method in detection performance is verified, and reliable support is provided for intelligent manufacturing and infrastructure diagnosis.

Example embodiments of the present disclosure relate to methods, devices, and computer storage medium for communication. A terminal device performs a first size alignment of a pair of DCI formats for multi-cell scheduling if a total number of DCI sizes configured to monitor a PDCCH is greater than a predefined number after a plurality of size alignments of a plurality of pairs of DCI formats for single-cell scheduling; and monitors the PDCCH from a network device based on the first size alignment. As such, an order for DCI size alignments may be determined and the total number of DCI sizes may be limited. The DCI formats for multi-cell scheduling may be aligned after size alignments of DCI formats for signal-cell scheduling, thus the blind detection complexity may be reduced and the detection performance may be improved.

The invention discloses an industrial product surface defect detection method based on feature coupling, and the method comprises the steps: 1) constructing a four-stage backbone feature extraction network, and integrating a DPSC module to expand a receptive field, thereby achieving the gradual feature learning from local to global; 2) designing a multi-scale feature fusion network, realizing effective fusion of different scale features, and performing deep coupling on high-level semantic information and low-level detail information; 3) constructing a multi-branch detection head to realize full-scale coverage; and 4) performing end-to-end training optimization: calculating a weight importance score by applying an LAMP pruning strategy, deleting redundant parameters, and remarkably reducing the model complexity and the calculation cost while keeping the detection performance. According to the method, through a global-local feature coupling mechanism, the technical problems that in industrial product surface defect detection, the defects are highly similar to the background, and the scale change is large are effectively solved, and high-precision and high-efficiency defect detection is achieved.

The invention discloses a dense small target detection method and device, a medium and equipment in the technical field of computer vision and target detection, and aims to solve the optimization problem of aerial photography dense small target detection. The method comprises the following steps: carrying out image preprocessing on acquired aerial image data to obtain an input image, and inputting the input image into a trained dense small target detection model: carrying out feature extraction through a trunk feature extraction network to obtain a multi-scale feature map; through a neck multi-scale feature fusion network, feature fusion and feature refinement are carried out on the multi-scale feature map, and multi-scale fusion features are obtained; and the small target detection layer is used for carrying out target detection on the multi-scale feature map to obtain a detection result. According to the invention, the detection performance of small target detection of the aerial image of the unmanned aerial vehicle can be improved, and robustness is increased for an algorithm.

The invention discloses a railway track defect detection method based on an MFGA-YOLO model, and the method comprises the following steps: inputting a data set containing a railway track defect image into the MFGA-YOLO model for training, inputting the collected railway track defect image into the MFGA-YOLO model to detect the railway track defect after the training is complete, and outputting a defect detection result; based on a YOLO11N architecture, multi-scale features are integrated through a hybrid aggregation network, calculation is accelerated and precision is maintained through a fast convolution gating unit, target matching is accurately evaluated through a self-adaptive fine-grained channel attention focusing key area and a Wise-ShapeIoU loss function, and therefore detection performance is remarkably improved. And the to-be-detected picture is input into the model to detect the railway track, so that the detection efficiency is improved, and meanwhile, the manual detection cost is greatly reduced.

The invention discloses a pavement throwing detection method and device based on deep learning, electronic equipment and a program product. The method is realized through a trained detection model, the model adopts an LMSADet detection head, a multi-scale feature extraction and space attention mechanism is introduced into a task branch, and multi-scale modeling is decoupled from a backbone network and a neck network and integrated to the detection head so as to fit a detection task to directly optimize local details and scale differences of a throwing target. In order to suppress background interference and improve the recognition effect of fuzzy boundaries, the neck network is added into an MSHA module so as to efficiently capture the semantic relation between the thrown object and the background and enhance the regional understanding ability. A C3ESP module is introduced into the backbone network, deep features are extracted through stacking depth separable convolution, and information loss is avoided in combination with residual optimization fusion; meanwhile, a PEMA attention mechanism is introduced, the importance of different receptive field features is dynamically adjusted, the model focuses on key features, data information is captured more comprehensively, and therefore the detection performance is remarkably improved.

Systems and methods for dynamic invocation of synthetic probes based on Real User Monitoring (RUM) agents include monitoring application performance metrics using a Real User Monitoring (RUM) agent embedded within a client application, wherein the RUM agent continuously observes and reports metrics indicative of user experience; detecting performance anomalies by analyzing application and network metrics against baseline performance thresholds established during normal operations; and initiating dynamic synthetic probes in response to the detected anomalies, wherein said synthetic probes are adaptively configured to target relevant destinations, adjust probing frequency, and utilize specific probing methods tailored to the characteristics and severity of the performance anomalies.

The invention discloses an infrared small target detection method based on a selective state space model and a related device, and belongs to the technical field of infrared small target detection, and the method comprises the steps: obtaining a to-be-detected infrared image, and carrying out the preprocessing of the to-be-detected infrared image, and obtaining a standard infrared image; inputting the standard infrared image into a pre-trained infrared small target detection model to obtain a detected infrared small target; the acquisition method of the pre-trained infrared small target detection model comprises the following steps: acquiring a historical infrared image sample set; preprocessing each infrared image in the historical infrared image set to obtain a standard historical sample set; the standard historical sample set is input into a pre-constructed detection model, a trained infrared small target detection model is obtained, the infrared small target detection model carries out feature extraction based on a selective state space model Mamba, feature fusion and enhancement are carried out through cross-scale feature interaction and a double attention mechanism, and the infrared small target detection model is obtained. The target detection performance can be effectively improved.

The invention discloses a pulse Doppler radar target detection method and system based on a background contrast attention mechanism, and relates to the field of radar target detection. The method comprises the following steps: preprocessing data to generate a distance Doppler image; constructing a neural network based on a background contrast attention mechanism; the neural network is trained, and the trained neural network is obtained; and obtaining a detection result of a pulse Doppler image to be detected by using the trained neural network. Based on a background contrast attention mechanism, the method focuses on learning clutter distribution characteristics of a background in a distance Doppler map and inhibiting background features from a feature level, thereby improving the significance of target features. A multi-scale background learner module based on a random mask and an attention mechanism module based on background contrast are designed in a targeted manner, and JS divergence is introduced into a loss function, so that background interference can be better suppressed from a feature level, and the detection performance in a low signal-to-clutter ratio scene is improved.

Example embodiments of the present disclosure relate to methods, devices, and computer storage medium for communication. A terminal device receives, in a low power (LP) mode and from a network device, an LP synchronization signal based on at least one of: an identity (ID) of a cell group of the network device, an ID of an LP-SS group of the network device, a tracking area ID associated with the network device, or a tracking area code associated with the network device; and detecting a wake-up signal from the network device based on the LP synchronization signal. As such, the terminal device can achieve and maintain the time synchronization with the network device in a certain precision, thus may utilize the synchronization to simplify the detection of wake-up signals, and enhance the detection performance.

The invention, which belongs to the image processing field, relates to an occlusion-sensing cage-rearing chicken detection method comprising the following steps: constructing a cage-rearing chicken image data set; constructing a multi-scale edge feature extraction module, performing multi-scale feature perception on an input image, enhancing edge features of each scale, performing splicing, screening out key edge features highly related to a target task, and further constructing representative feature representation; a context guide feature fusion module is constructed, and local features and surrounding context information are fused while down-sampling is carried out; a multi-scale shielding perception detection head is constructed, and the perception capability of a shielding scene is enhanced; constructing a target detection network model, and performing training and evaluation; and inputting newly collected cage-rearing chicken image data into the trained target detection network model to realize accurate detection of a cage-rearing chicken target. According to the method, the chicken detection performance of the model in a complex cage rearing environment is remarkably improved, and expression of detail features such as feather textures and cockscomb contours is enhanced.

The invention provides an underwater sonar target detection system and method. The underwater sonar target detection system comprises an optical fiber hydrophone array module which comprises a plurality of optical fiber hydrophone units, each unit comprises an optical fiber Bragg grating sensor and a sound pressure signal demodulation device, and the optical fiber hydrophone array module is used for collecting underwater sound wave signals in real time and converting the underwater sound wave signals into first electric signals; the sonar transmitting-receiving array module comprises a broadband sonar transmitter, a multi-beam receiver and a signal preprocessing circuit, and is used for actively transmitting a frequency modulation continuous wave signal, receiving a target reflection echo and generating a second electric signal; and the multi-mode signal fusion processing module comprises a high-speed data acquisition card, a self-adaptive noise suppression unit and a time-frequency analysis unit. The underwater sonar target detection system and method provided by the invention have the advantages of relatively high reliability, relatively high detection performance, relatively accurate cross-modal signal fusion and capability of performing intelligent target identification and tracking.