3654 results about "Small target" patented technology

The invention discloses a deep learning-based small target defect recognition model training method, relates to the technical field of defect recognition model training, and aims at meeting small defect detection requirements, starting with high-resolution diversified data construction and accurate labeling, highlighting weak targets through multi-scale feature fusion and spatial attention, and realizing high-resolution target defect recognition. A hard case scene is processed in cooperation with layer-by-layer screening and secondary intensified training, real-time iterative optimization is achieved through multi-model fusion and online dynamic adjustment and optimization, finally, multi-mode and time sequence dimensions are expanded to capture deeper and dynamic defect information, the missing detection and false detection rate is greatly reduced, and the detection efficiency is improved. The detection efficiency and adaptability of micron-sized defects under a complex process background are improved; furthermore, by means of multi-source data such as infrared, X-ray or 3D morphology and a time sequence modeling means, multiple dimensions are fused, and hidden or early cracks are brought into a detection and prediction range, so that a high-reliability and evolvable intelligent recognition system for the tiny target defects is constructed.

A ground visual question-answering method based on dynamic dual-level visual information fusion includes using a dual-level multiscale network, which is divided into language-guided pixel-level features and region-level features. These two scale branches are combined to predict the final textual answer and ground answer. Furthermore, a question-guided dynamic region-level feature localization network is proposed to locate visual information guided by the question and adaptively assign masks of different sizes to ground answers, thereby enhancing the accuracy of locating and segmenting small targets. Additionally, a cross-modal aggregation module is designed to fuse features from both levels, enhancing the fusion of pixel-level and region-level features to improve the segmentation effect of ground answer masks' edges. The ground visual question-answering system built by the language-guided adaptive dual-level feature fusion network in this invention can effectively improve the accuracy of the entire model while answering questions and generating answer ground masks simultaneously.

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 unmanned aerial vehicle small target aerial photography detection method and system based on RT-DETR, a medium and equipment, and belongs to the technical field of unmanned aerial vehicle visual detection, aerial photography images are collected based on an unmanned aerial vehicle, the images are input into a trained target detection model, and target position and category information is obtained. The method comprises the following steps: firstly, extracting low-layer features of an image, and simultaneously capturing information of a channel dimension and a space dimension based on an efficient multi-scale attention mechanism; and outputting features through the convolution residual block. Key features are processed based on a single-scale feature interaction module; for the coded feature map, fusing information of a shallow layer and information of a deep layer step by step in an up-sampling and transverse connection mode; in a down-sampling stage, context semantic information of a target is reserved, and global and local information interaction is enhanced. According to the method, the accuracy and robustness of small target detection are remarkably improved, and the practicability and deployment value in actual application scenes such as unmanned aerial vehicle aerial photography and remote sensing monitoring are expanded.

The invention discloses an underwater small target detection method based on YOLOv8 improvement, and aims to solve the problem of small target detection caused by illumination variation, background complexity and target scale difference in an underwater environment. According to the method, optimization is carried out based on a YOLOv8 architecture, firstly, a star convolution StarsBlock in a StarNet backbone network is combined with a C2F module of the YOLOv8, an improved C2FStarsBlock module is obtained, and the diversity and robustness of feature extraction are remarkably improved. Secondly, a CARAFEEnhanced module is adopted to replace up-sampling operation of a YOLOv8 neck, and dynamic convolution, grouping convolution and a nonlinear feature enhancement strategy are introduced, so that the calculation efficiency and adaptability of the model are optimized; and finally, integrating a gradient-guided multi-scale convolution attention mechanism GMSAA, introducing the gradient-guided attention mechanism in a back propagation stage, accurately focusing on a target area, and reducing background interference. The method effectively improves the detection precision of the underwater small target, and is especially suitable for efficient automatic detection of small underwater targets such as marine organisms.

The invention discloses a sonar image underwater detection method and system based on multi-scale feature fusion and a college up-sampling algorithm, and the method comprises the steps: collecting the foresight sonar data of an underwater typical target, and making a detection training and testing data set of a sonar image; the method comprises the following steps: constructing a feature fusion network comprising an efficient upsampling module, a multi-scale feature fusion module and a small target detection branch, constructing a target detection algorithm model based on YOLOv11, taking the feature fusion network as a neck network of YOLOv11, and removing a deep feature map detection branch of YOLOv11; and training a target detection algorithm model by using the manufactured data set, and inputting a foresight sonar image into the trained model for reasoning to obtain a detection result of the underwater typical target. According to the invention, the accuracy and robustness of an underwater target detection algorithm are improved.

The invention discloses a multi-modal visual fusion complex scene small target detection tracking method and system, and relates to the technical field of unmanned aerial vehicle target tracking, and the method comprises the steps: employing a visible light camera, an infrared thermal imager and a laser radar sensor which are carried on an unmanned aerial vehicle platform, and synchronously collecting RGB images, thermal infrared images and point cloud data; the consistency of the multi-modal data is ensured through data preprocessing and space-time alignment; constructing a lightweight double-branch network to extract multi-scale features, generating a fusion feature map by adopting adaptive weighted fusion, and generating depth information by utilizing point cloud to assist in scale estimation; a small target detection head is designed based on the fusion feature map, and precise detection is realized in combination with a feature pyramid network, adaptive scale prediction and a context awareness suppression mechanism; furthermore, through multi-mode cooperative tracking, including target association, spatio-temporal context modeling, trajectory prediction and a re-detection mechanism, tracking continuity is ensured.

The invention relates to the technical field of material detection, in particular to an optimization method for industrial pure iron metallographic specimen defect detection based on YOLOv8obb. The method comprises the following steps: acquiring an industrial pure iron metallographic sample defect image; preprocessing the metallographic image; constructing a YOLOv8obb defect detection network, and optimizing the defect detection network to obtain an optimized defect detection model; according to the model, a DynamicConv is used as a convolutional layer of Bottleneck in a C2f module, a SimAM attention mechanism module is introduced into a backbone network, a tiny target detection head is added, and an NEFPN feature pyramid network is used as a neck multi-scale feature fusion network; and the preprocessed sample data image pair is used for performing defect detection model training, and the trained optimized defect detection model is used for performing industrial pure iron metallographic image defect detection, so that the detection precision and the detection efficiency are improved.

The embodiment of the invention provides an unmanned aerial vehicle aerial photography target detection method and device based on deep learning. The method is applied to the technical field of target detection, and comprises the following steps: acquiring aerial image data of an unmanned aerial vehicle, and preprocessing the image data; inputting the preprocessed image data into a deep learning-based feature extraction network, wherein the deep learning-based feature extraction network comprises a backbone network, a small target frequency domain enhancement module, a lightweight multi-scale modeling module, a hierarchical context sensing module and a convolution gating linear module; and the preprocessed image data is analyzed and processed through the feature extraction network based on deep learning, and a target detection result is output, so that the real-time performance and precision of target detection are improved.

The invention provides a remote sensing strip mine area detection method based on a double-branch structure and a feature fusion mechanism, and the method comprises the steps: obtaining multi-source high-resolution remote sensing image data of a strip mine mining area, carrying out the preprocessing of atmospheric correction, radiation calibration, color correction, image registration, cutting operation and the like, and obtaining time sequence remote sensing image data; a deep learning algorithm is adopted to construct a strip mine area detection model based on a double-branch structure and a feature fusion mechanism, training is carried out through the time sequence remote sensing image data, a remote sensing image detection model is obtained, the double-branch structure comprises a feature extraction branch and a feature generation branch, and the feature extraction branch comprises a feature extraction branch and a feature fusion branch; the feature fusion mechanism comprises a cross attention fusion module and a feature adaptive fusion module; inputting to-be-detected remote sensing image data into the remote sensing image detection model to obtain a detection result of the strip mine mining area. According to the method, the recognition precision of small target details and mining area boundaries of low-resolution images is improved, and the calculation efficiency and the detection precision are both considered.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing target detection method and system for a low-visibility image. The method comprises the following steps: acquiring multi-modal remote sensing image data; carrying out defogging enhancement processing on the low-visibility input image; normalizing the defogged RGB image and the defogged IR image, and then splicing and fusing the RGB image and the IR image; carrying out layer-by-layer coding on the multi-modal fusion image by adopting a mixed trunk structure fusing Transform, Mamba and CNN (Convolutional Neural Network); performing frequency domain decomposition on the trunk output features based on two-dimensional wavelet transform; generating an HR feature map by adaptively selecting a key region; and carrying out cross-scale aggregation on the HR feature map to obtain a detection target frame. Through the multi-modal image defogging enhancement and feature distillation mechanism, the definition and contrast of the remote sensing image in severe weather such as haze and rainy days are effectively enhanced, the shielding interference of environmental degradation on small target detection is weakened, and the stability and adaptability of the model in complex weather scenes are enhanced.

The invention discloses an unmanned aerial vehicle image small target detection method based on dynamic filtering and an adaptive sparse Transform. According to the method, an end-to-end target detection framework is adopted, a dynamic filtering module is introduced into a backbone network, global feature interaction is achieved through data-dependent frequency domain operation, and linear calculation complexity is maintained. For feature interaction in a scale, an adaptive sparse Transform module is introduced to enhance the capability of focusing key information on high semantic hierarchy features of a model, and noise interference and feature redundancy are effectively suppressed at the same time. Through the combination of dynamic filtering and adaptive sparse Transform, the model can extract image foreground information more effectively on the premise of not significantly increasing the calculation burden, and the problem that a traditional target detection model is susceptible to complex background interference is significantly relieved.

The invention discloses a real-time single-stage remote sensing image correction target detection method based on YOLOV8, and relates to the technical field of remote sensing image processing. According to the method, a deformable convolution dynamic prediction local geometric distortion parameter is embedded based on a YOLOv8 backbone network, an adaptive deformation field is generated, pixel-level real-time correction is realized, shallow details and high-level semantic features are fused through a bidirectional path aggregation network, and channel attention and a space gating mechanism are combined, so that the real-time correction of the image is realized. The small target detection capability is enhanced, background noise is suppressed, angle prediction is divided into discrete classification and continuous residual error regression tasks through a decoupling type rotation detection head, angle periodic errors are eliminated in combination with a direction sensitive loss function, and the rotation frame positioning precision is improved. And constructing a dynamic multi-task collaborative loss function, introducing gradient distribution consistency constraint to jointly optimize correction and detection tasks, and realizing feature semantic alignment and model self-enhancement through end-to-end closed-loop training. And the rotating target detection precision and the complex scene robustness are obviously improved.

A surface defect detection method for multi-scale and small target features comprises the following steps: step 1, acquiring a surface defect data set, converting the format of a labeling file into a required format, and dividing an image into a training set, a verification set and a test set; step 2, constructing a DMS-YOL O surface defect detection model; 3, training the constructed DMS-YOLO surface defect detection model, and training the surface defect detection model by using the training set and the verification set to obtain an optimal surface defect detection model; 4, performing evaluation on the test data set through the trained model; 5, fully testing and debugging the model trained in the step 3 to ensure the accuracy and stability of the surface defect detection method in different scenes; and step 6, deploying the trained DMS-YOLO surface defect detection model in an actual industrial production environment, performing defect detection on the surface of a real product, and recording the accuracy rate, the false detection rate and the missing detection rate of a detection result. And 7, summarizing the whole detection process, forming a standardized solution, and popularizing the standardized solution to different industrial fields for application verification to ensure that the method has practicability and stability.

The invention belongs to the technical field of PCB defect detection, and discloses a PCB small target defect detection method based on improved YOLOv8x. Performing data enhancement on the original PCB defect picture data set; a Backbone network is improved, a SAConv convolution kernel is introduced, and a fusion MLCA mixed local channel attention module is added; a Neck network is improved, a small target detection head for processing a 160 * 160 pixel feature map is added, in addition, a WTconv convolution kernel is introduced into the Neck network, and an SCSA fusion space and channel collaborative attention module is added; and performing PCB small target defect detection by using the trained improved YOLOv8x model. The method is suitable for defect detection of small targets in pictures under the complex background of the pictures, and compared with an original YOLOv8x model, the method has the advantage that the detection precision is improved on the premise that the detection speed is not affected.

The invention relates to an unmanned aerial vehicle aerial image small target detection method and a computer readable storage medium. The method comprises the steps of obtaining unmanned aerial vehicle aerial image data, and dividing the data into a training set and a verification set after processing; yOLOv8n is used as a basic model, traditional convolution structures of a shallow layer and a middle layer are replaced by full-dimensional dynamic convolution in a backbone network of the YOLOv8n model, an enhanced space attention mechanism based on routing is introduced into the backbone network of the YOLOv8n model, and an original SPPF structure of the backbone network is replaced by a multi-scale context modulation module. Performing collaborative design of a multi-scale structure and a detection head in a neck network and a head network of the YOLOv8n model to obtain an improved YOLOv8n model; training and verifying the improved YOLOv8n model by using the training set and the verification set to obtain a trained unmanned aerial vehicle aerial image small target detection model; and inputting a to-be-detected unmanned aerial vehicle aerial image into the trained unmanned aerial vehicle aerial image small target detection model to obtain a detection result. According to the invention, the small target detection precision and speed are improved.

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 a PCB defect detection method based on a small target enhanced feature pyramid, and the method comprises the steps: S1, data collection and processing: downloading a PCB defect data set for screening, carrying out the data processing of a screened picture, and dividing the processed data set into a training set and a verification set; s2, constructing and training a model: constructing an improved RT-DETR-R18 model and carrying out model training to obtain the weight of the improved RT-DETR-R18 model, and then carrying out model verification; and S3, detection and visualization: deploying the verified model to a visual interface for real-time detection of PCB defects. According to the method, an improved RT-DETR-R18 model algorithm is adopted, and higher accuracy and robustness are achieved; and the improved network structure also has relatively high hardware adaptability and compatibility, and can be used in PCB defect detection tasks in different scenes.

The invention belongs to the technical field of computer vision and artificial intelligence, belongs to the cross technical field of target detection, deep learning and image processing, and particularly relates to a YOLOv8 algorithm improvement method based on an unmanned aerial vehicle aerial image small target detection model, which comprises the following steps of: introducing a user-defined feature enhancement module into a YOLOv8 backbone network, a neck part and a detection head part; the self-defined feature enhancement module comprises a context guide self-adaptive fusion module introduced into a backbone network so as to replace part of traditional convolution operation; a space edge sensing feature up-sampling module and a space sensing enhanced convolution module are adopted in the neck fusion network; a fine-grained dynamic pruning detection head is introduced into a detection head detection network. According to the method, the performance of the model in a small target detection scene is effectively enhanced, and the accuracy, robustness and real-time response capability of a detection system are remarkably improved.

The invention provides an unmanned aerial vehicle aerial image small target detection method, system and device and a medium. The method comprises the implementation steps that an unmanned aerial vehicle aerial image data set is acquired and preprocessed; yOLOv8s is used as a basic network, and SPD convolution is introduced into a backbone network; the neck structure is improved, and the new structure is named as a fused context feature alignment pyramid network (FCAFPN); a feature fusion module SIAF is introduced into the feature fusion module; then, a dynamic up-sampling module (DySample) is used for replacing nearest neighbor sampling (NN); an improved YOLOv8 network is obtained; the improved YOLOv8 network is applied to unmanned aerial vehicle aerial image recognition, and a high-precision small target detection deep learning model is constructed through training; inputting a to-be-detected image into the model for detection to obtain a target detection result; the system, the equipment and the medium are used for implementing the method. According to the invention, accurate identification of the small target of the unmanned aerial vehicle is realized.

The invention discloses an infrared small target detection method fusing local prior and a multi-scale global background, and the method comprises the steps: firstly obtaining image data containing an infrared image and a mask label corresponding to the infrared image, and carrying out the preprocessing; secondly, a target detection model of an encoder-decoder architecture is constructed, an encoder comprises a local detail prior mining branch and a multi-scale global background perception branch which are parallel, step-by-step feature extraction is performed on the preprocessed image data, and a decoder comprises a progressive feature fusion decoding branch; and inputting the features of each level of the encoder double branches into decoder branches for decoding step by step to obtain a detection result. And finally, a weighted depth supervision mechanism is introduced in training, auxiliary prediction output is set in a plurality of decoding layers, and weighting loss is calculated. According to the method, the problems of insufficient local detail modeling, insufficient multi-scale global background perception of Mamba, difficulty in global and local feature fusion and the like in the existing method are solved, and the detection precision of the infrared small target is improved.

The invention relates to a small target detection method and device and a storage medium, and the method comprises the steps: obtaining a to-be-processed image; inputting an image to be processed into the trained small target detection model; wherein the small target detection model comprises an enhanced feature extraction network and a detail feature optimization module; performing local feature extraction processing on the to-be-processed image through a plurality of context sensing aggregation units in the enhanced feature extraction network to generate a plurality of local feature maps and deep feature maps with different resolutions, and fusing the plurality of local feature maps to generate a global enhanced feature map; performing detail optimization processing on the input global enhanced feature map and the deep feature map through a detail feature optimization module to generate a prediction layer feature map; and obtaining a target detection result based on the prediction layer feature map. According to the invention, the problem of low detection precision of the small target is solved, the detection capability of the small target is enhanced, and the detection accuracy of the small target is remarkably improved.

The present disclosure discloses a small target detection method based on improved YOLOv8, including: inputting a small target image to be detected into a pre-trained small target detection model based on the improved YOLOv8 for identification to obtain a detection result, where a method for training a small target detection model based on the improved YOLOv8 includes: acquiring a small target image data set and dividing the small target image data set into a training set and a validation set; replacing a backbone network of YOLOv8 with a backbone network ATDeNet and constructing the small target detection model based on the improved YOLOv8; and training the constructed small target detection model by using the training set and the validation set to obtain a trained small target detection model based on the improved YOLOv8. The accuracy and efficiency of small target detection can be significantly improved.

The invention discloses an SAR (Synthetic Aperture Radar) small-scale target detection system and method based on deep learning, and aims to solve the problems of low detection precision, inaccurate positioning and poor robustness of a small-scale target under a complex background. The system performs preprocessing through adaptive guided filtering and improved multi-scale local self-adaption to generate a high-quality candidate region; a content perception feature recombination network is combined with a lightweight multi-head attention converter and a dynamic deformable fusion unit, cross-level dynamic aggregation of shallow texture and deep semantics is achieved, and the small target perception ability is enhanced; a modified Bhattacharyya distance loss function is introduced to optimize bounding box matching, and the small target positioning precision is remarkably improved. In addition, the system adopts a multi-stage cascade detection architecture to gradually optimize candidate target screening and regression, and the recall rate is improved through weak target recovery and geometric consistency verification. The multi-source data fusion module combines polarization characteristics and optical image information to enhance the cross-modal detection capability.

The invention discloses a surface defect small target detection method based on multi-scale feature interaction. Comprising the steps that a surface image, collected in real time, of a to-be-detected product is input into a detection model, and after the detection model processes the surface image, an image marked with possible defect classification categories, bounding box coordinates and defect target existence confidence is output; wherein the detection model is a model taking a YOLOv11 network as a basic structure and comprises a backbone network, a neck network and a detection head which are sequentially connected in series; a multi-scale feature extraction SMK module and an attention mechanism SELA-S module are introduced into the backbone network; two feature fusion Fault modules are introduced into the neck network. According to the method, the multi-scale target features under the complex background can be effectively extracted and fused, and the method has good processing performance for distinguishing defects in the picture from the complex background and multi-scale target detection. And in a surface defect detection task, the method can be accurately focused on a defect area.

The invention discloses a self-attention mechanism-based small target detection method under a visual angle of an unmanned aerial vehicle, and the method comprises the following steps: S1, obtaining an image data set suitable for the visual angle of the unmanned aerial vehicle, and carrying out the preprocessing of the image data set; s2, constructing a small target detection model based on a self-attention mechanism; s3, sending the preprocessed image data set into a small target detection model for training; and S4, detecting a to-be-detected target by using the trained small target detection model and outputting a result. According to the invention, feature extraction is carried out through the feature extraction module, the local self-attention module and the cross-scale self-attention mechanism are combined, the small target recognition capability of the model can be effectively improved, the robustness and real-time performance of the network are further enhanced by adopting the decoder and the adaptive loss function, and finally efficient and accurate small target detection is realized.

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 discloses a power grid infrastructure target detection method and device based on MAMBA-YOLO, and a medium, combines a Mama architecture and a YOLO target detection technology, and solves the bottleneck problems of target detection precision and calculation efficiency in a power grid infrastructure image by introducing an SSM to optimize a YOLO model. Firstly, feature maps of different levels are extracted from input data through a feature extraction module, and then feature fusion is performed by using a neck path aggregation network. And the fused feature map is sent to a target positioning and classification module for object classification and bounding box regression. The method can efficiently identify targets such as equipment, facilities and lines in power grid infrastructure. Through a multi-direction scanning and feature fusion strategy, targets of different scales can be accurately detected under a complex background, and the method has significant advantages especially for small target detection. Compared with a traditional YOLO model, the Mamb-YOLO model has the advantages that the calculation complexity is remarkably reduced while the detection precision is guaranteed, and the requirement of power grid infrastructure target detection for real-time performance is met.

The invention discloses an electric power scene defect small target detection method based on Gaussian mask supervision and cross-layer attention guidance, and the method comprises the steps: inputting an electric power scene image into a detection model, extracting an initial feature map through a backbone network, carrying out the multi-stage feature extraction of the initial feature map according to a convolution path, and carrying out the multi-stage feature extraction of the initial feature map; processing the multi-stage features based on a path aggregation network, and outputting a plurality of fusion feature maps with different feature levels from shallow to deep; and based on cross-scale window attention, guiding a shallow fusion feature map to carry out semantic information modeling by using a deep fusion feature map with high semantics in every two adjacent fusion feature maps, and after a plurality of output feature maps are obtained, respectively processing and outputting prediction results by using a multi-branch detection head. According to the method, shallow feature activation prediction and cross-scale window attention guidance are fused, and the detection robustness and positioning precision of a tiny fault target in an unmanned aerial vehicle inspection image can be effectively improved.

The invention discloses an unmanned aerial vehicle aerial image small target detection method based on RT-DETR, an end-to-end target detection algorithm RT-DETR is improved, an FCW-RTDETR algorithm is provided, and the method comprises the following steps: improving a backbone network by using adaptive frequency cavity convolution, and extracting unmanned aerial vehicle aerial image features; performing intra-scale feature interaction AIFI on a P5 layer in the backbone network, and inputting obtained features into a cross-scale feature fusion network CCFM; the method comprises the following steps: respectively improving a CCFM structure and an up-and-down sampling method by using a context and spatial feature calibration network CSFCN and wavelet pooling, and fusing features of different scales in the CCFM to obtain an image feature sequence; selecting a feature from a feature sequence output by an encoder as an initial target query of a decoder by using IoU-perceived query selection; and the decoder iteratively optimizes the target query through the auxiliary prediction header, generates a bounding box and a confidence score, and completes small target detection. According to the scheme, the small target detection efficiency and accuracy are improved.