3084 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.

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 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 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 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.

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 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 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 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 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 relates to an aliasing image intelligent deformation small target identification algorithm based on detector multiplexing, and relates to the technical field of aliasing image target identification, and the algorithm comprises the steps: designing a detector multiplexing optical system; designing a view field coding element, and splicing the whole system; simulating and generating a motion track of the early warning target under the space-based background; the target motion trail is imaged through a detector multiplexing optical system; aliasing video simulation of moving target imaging is carried out; an intelligent deformation small target recognition algorithm is applied to the aliasing image, and a moving target is recognized; target resolution and field-of-view positioning are realized by using the relevance between the inter-frame track of the moving target and the light spot shape. According to the aliasing image intelligent deformation small target recognition algorithm based on detector multiplexing, light originally imaged on a large-area-array detector is folded and imaged on a small-area-array detector, and therefore the purpose that the small-area-array detector receives large-view-field imaging is achieved.

The invention relates to an aerial photography target detection method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring an aerial image acquired by an image sensor; inputting the aerial image into a pre-trained target detection model to obtain a target detection result; the target detection model comprises a backbone network, a neck network and a head network; wherein the backbone network is used for performing feature extraction on an aerial image to obtain image extraction features; the neck network is used for performing multi-scale fusion on the image extraction features to obtain a plurality of fusion features of different scales; the neck network comprises a bidirectional information transfer network, the bidirectional information transfer network comprises a separation and attention enhancement module, and the separation and attention enhancement module is used for generating fusion features corresponding to tiny targets; and the head network comprises a detection head corresponding to the tiny target and is used for obtaining a target detection result corresponding to the fusion feature based on the fusion feature. By adopting the method, the detection accuracy under the complex aerial image can be improved.

The invention discloses a small target detection and state perception method based on multi-scale feature fusion, and belongs to the field of computer vision and deep learning. Multi-scale semantic features are extracted through a backbone network; two uplink fusion paths and two cascaded downlink enhancement paths are constructed, and multi-scale feature fusion is performed, so that the perception capability of targets with different sizes is enhanced, and the accuracy and robustness of detection are improved; and meanwhile, a regional state sensing mechanism is constructed based on a detection result, continuous monitoring and intelligent analysis of target space distribution, behavior trend and dynamic change are realized, and the adaptability and response speed of the system in a complex environment are improved. The method gives consideration to the detection precision and the calculation efficiency, and is suitable for real-time application scenes with limited resources.

The invention provides a vortex aircraft detection signal processing method and system based on optical fiber sensing. The method comprises the following steps: firstly, acquiring an original micro-vibration signal; secondly, performing environment self-adaptive cooperative processing on the original micro-vibration signal to generate a self-adaptive micro-vibration signal; converting the generated self-adaptive micro-vibration signal into a spatio-temporal evolution sequence based on eddy current physical characteristics; then analyzing the spatio-temporal evolution sequence to output a type discrimination result of the vortex aircraft; and finally, generating a customized detection report based on the type discrimination result. According to the technical scheme provided by the invention, the detection and identification capability of a low-altitude low-speed small target in a complex environment is improved, and reliable detection and identification of the vortex aircraft in the complex environment are also realized.

The invention relates to the technical field of image processing, and provides an enteromorpha remote sensing image detection method and system based on deep learning, and the method comprises the following steps: carrying out the edge gradient extraction and small target enhancement processing of an obtained to-be-detected remote sensing image, and obtaining a first feature map after the edge enhancement and small target feature enhancement; transmitting the first feature map to a U-shaped backbone network formed by cascading multiple stages of Ep-VSS block modules, performing multi-stage feature extraction, and obtaining a detection result of the enteromorpha remote sensing image based on the feature map output by the last stage of Ep-VSS block module; according to the method, through edge enhancement, small target sensitive detection, multi-scale texture extraction and spatial context modeling, precise boundary segmentation and long-range dependence modeling are realized, and the accuracy, real-time performance and reliability of enteromorpha remote sensing monitoring are improved.

The invention discloses a power transmission line foreign matter detection method and system based on multi-modal image fusion, and relates to the technical field of intelligent operation and maintenance and state monitoring of a power system, a lightweight Ev-Mama architecture is introduced into a backbone network part of YOLOv13, the model keeps relatively low calculation complexity, and meanwhile, the power transmission line foreign matter detection efficiency is improved. And the modeling capability of the method on the long-range dependency relationship and the global semantic information is obviously enhanced. Besides, by using the CDIDF module, the EVCS module and the MHSAA module, on the basis of increasing a small amount of calculation, the scale sensing ability, the space structure modeling ability and the context understanding ability of the model are effectively improved, and the performance bottleneck of a traditional YOLO series network in the aspects of processing small targets, shielding targets and cross-scale information fusion is effectively relieved.

The invention discloses an improved YOLOv5 protective equipment detection method combining channel selection and an attention mechanism, and the method comprises the steps: replacing an original spatial pyramid pooling fusion module SPPF of a YOLOv5 backbone network part with a channel selection multi-scale fusion module CSMF, and enabling the channel selection multi-scale fusion module CSMF to be based on a feature adaptive weighted fusion strategy of a multi-channel selection mechanism. According to context information and semantic distribution of input image features, fusion weights of feature channels under different receptive fields are dynamically adjusted, so that more discriminative multi-scale feature integration is realized. According to the invention, the adaptive capacity of the model to multi-scale targets is improved through the CSMF module, and the problem of inaccurate recognition of small targets such as gloves by a traditional model is solved; after the CBAM is adopted to enhance the MBconv module, the model can more effectively distinguish a key area and a background area in an image, and the robustness of target shielding and background interference in a complex scene is enhanced.

The invention discloses an automobile central control screen small target detection method based on YOLOv11 improvement, and the method specifically comprises the steps: S1, generating an image data set, carrying out the preprocessing and enhancement, and dividing the data set; s2, an improved C3k2GCConv module, a WFU module and a CGAFusion module are introduced, and a YOLOv11 network model is constructed; s3, training the model by adopting a cosine annealing learning rate and a mixed precision training strategy; s4, inputting a to-be-detected central control screen image into the improved YOLOv11 detection model, and outputting the category and bounding box coordinates of a target; and S5, performing screening and optimization through a post-processing module, and finally outputting a detection result in the form of a bounding box and a category label. According to the method, by improving the YOLOv11 model, a display target can be effectively recognized in complex environments such as strong light direct incidence, screen reflection and dim light, and the stability and robustness of the model in a complex illumination scene are improved.

The invention provides an end-to-end tiny target detection method, and aims to solve the problems of missing detection and false detection of tiny targets caused by interference of sparse features, halo, noise and the like. According to the method, a TINYDETR model is constructed, and the TINYDETR model is composed of an HGNetv2 backbone network, an LGFSI module, an SO-CSFF module and a decoder with an auxiliary prediction head. Wherein the LGFSI module realizes global-local information interaction through joint modeling of a frequency domain and a spatial domain, and background interference is effectively suppressed; the SO-CSFF module enhances the fusion of shallow details and deep semantics through a bidirectional feature flow mechanism, and enhances the feature expression of a tiny target. After the model is trained and optimized, high-precision detection of a tiny target can be realized.

The invention discloses a semiconductor mechanical arm autonomous navigation and positioning method based on AI vision, and relates to the technical field of mechanical arms, and the method comprises the following specific steps: visual perception and multi-modal information fusion: collecting multi-modal data through configured industrial, depth, light field and polarized light cameras, pre-processing the multi-modal data, and carrying out multi-modal information fusion; realizing information fusion by using a deep learning network containing a feature extraction layer and a fusion layer and an attention mechanism, and outputting a unified visual description; according to the method, a deep learning target detection model is utilized, an attention mechanism is introduced to enhance the attention and extraction capability of small target features, the recognition result is optimized in combination with surface feature information recognized by polarized light imaging, the category and position of a target object can be accurately determined, meanwhile, a mechanical arm motion error model is established, and the recognition accuracy is improved. And the fused visual positioning information is fused with sensor data such as a mechanical arm joint encoder and a gyroscope by utilizing a sensor fusion technology, and a positioning result is optimized and compensated.

The invention discloses a remote sensing image-oriented multi-scale adaptive small target detection system and method. The system comprises a backbone network backbone, a feature aggregation network Neck and a detection head Head, the backbone network backbone is used for extracting multi-scale effective features, the feature aggregation network Neck is used for fusing and enhancing the multi-scale effective features, and the detection head Head is used for making a decision for target detection. The method comprises the following steps: constructing a target detection data set by using remote sensing images, preprocessing the images, and dividing the images into training, testing and verification sets; a backbone network backbone is adopted to extract multi-scale effective features, and a neck network Neck is adopted to refine the extracted features; carrying out mixed loss training by adopting NWD loss; and the detection head Head outputs the category and location of the target according to the results of the classification branch and the regression branch. According to the method, the loss of information in the transmission process is reduced, the background noise is inhibited, and the accuracy of small target detection of the remote sensing image is improved.

The invention discloses a small target recognition method and system for a multi-modal fusion image in a complex environment, and belongs to the technical field of computer vision and image recognition, and the method comprises the steps: obtaining a visible light image, an infrared image and environment sensor data; image registration is carried out on visible light and infrared images, and a multi-scale image feature pyramid is constructed. And respectively extracting visible light and infrared image features to obtain visible light and infrared imaging feature data. And performing multi-modal data fusion on the visible light and infrared imaging feature data based on a cross-modal attention mechanism, and adaptively adjusting a fusion weight based on environmental sensor data to generate fusion features. And performing space-time enhancement processing on the fusion feature to obtain an enhanced fusion feature. And performing target tracking detection on the small target, and outputting position and category information of the small target. According to the method, the small target recognition capability in a severe environment is remarkably improved, and high precision and robustness can still be kept in a foggy, low-visibility and dark scene.

The invention relates to the technical field of computer vision and deep learning, in particular to an aerial image target detection method based on frequency domain decoupling multi-scale feature fusion, which comprises the following steps of: acquiring an aerial image of an unmanned aerial vehicle, establishing a data set, and performing preprocessing and data division; an aerial image target detection network is constructed, and the aerial image target detection network receives an input image and outputs a target category and a bounding box position; loss functions are determined, wherein the loss functions comprise classification loss representing matching quality, coordinate loss representing prediction coordinate relevancy and bounding box regression loss representing bounding box positioning accuracy; training the aerial image target detection network based on the data set and the loss function; inputting a to-be-detected aerial image into the trained aerial image target detection network to obtain a to-be-detected target category and a bounding box position; the method can improve the feature fusion degree, retains high-frequency details, and enhances the small target recognition rate.