2033 results about "Small target" patented technology

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 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 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 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 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 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.

The invention provides a multi-small-target tracker and tracking method based on SAM2, and the method comprises the steps: dividing a video into a plurality of segments, and enabling the last frame of a previous video segment to be overlapped with the first frame of a next video segment; performing target detection on a first frame of the initial video clip, and allocating an ID to a detected target object; target tracking is executed in each video clip through SAM2, target detection is executed on overlapped frames between adjacent video clips, a detected bounding box is matched with a mask output by SMA2 through a previous video clip according to a mask-to-detection association strategy, and target tracking is executed in each video clip through SAM2; therefore, when a new target appears in the next video clip, a new ID can be allocated to avoid tracking interruption. According to the method, the mask and the detection are located at the same space-time position through video frame overlapping, so that tracking cannot be interrupted as long as the appearance of the target can still be visually distinguished, and the tracking failure rate when the size of the target is too small or the camera is zoomed and moved can be remarkably reduced.

The invention discloses a target detection method based on a YOLO model, electronic equipment and a storage medium, and relates to the technical field of target detection. Comprising the following steps: inputting an aerial image of an unmanned aerial vehicle into a trained target YOLO model; the target YOLO model comprises a backbone network, a neck network and a head network, and a feature extraction module in the backbone network performs multi-scale feature extraction by adopting a double-branch architecture attention mechanism; performing multi-scale feature extraction on the aerial image by adopting a dual-branch architecture attention mechanism through a feature extraction module in the backbone network, and constructing to obtain a plurality of layers of first comprehensive image features of the aerial image; performing feature fusion on the first comprehensive image features of different levels through a neck network to obtain second comprehensive image features of multiple levels; and inputting the multiple levels of second comprehensive image features into a head network to obtain a target detection result of the aerial image. According to the invention, the accuracy of small target detection can be improved.

The invention provides a small target detection method and system based on aligned visible light and infrared images, and relates to the field of target detection, and the method comprises the steps: obtaining a visible light image and an infrared image of a to-be-detected small target; inputting a visible light image and an infrared image into the trained detection model, firstly, respectively performing multi-scale feature extraction on the visible light image and the infrared image by adopting a double-branch structure, and in the extraction process, performing multi-scale feature extraction on the visible light image and the infrared image through interactive collaborative learning of the visible light image and the infrared image; the method comprises the following steps: firstly, carrying out feature alignment, modal interaction correction and multi-scale feature enhancement between two modals to obtain enhanced visible light features and infrared features, then carrying out feature fusion, and finally, carrying out small target positioning and classification by utilizing the fused features. According to the method, feature level alignment of image pairs is realized by using deformable convolution and modal interaction correction, so that the features of visible light and infrared images are effectively and fully interacted and fused, and the accuracy of a detection algorithm is improved.

The invention discloses a human body infrared image small target detection method based on an improved FGLCM feature, and relates to the technical field of image processing and target detection, and the method comprises the following steps: building a boundary singularity auditing baseline under a unified time baseline, carrying out the multi-scale energy mapping of a curved surface fitting residual error of a human body infrared image, and generating a traction residual error distribution diagram; and constructing a reflection pseudo peak discriminator based on the traction residual distribution diagram, and extracting a pseudo peak kernel position by combining polarization sensitivity estimation and view angle transformation consistency constraint. According to the method, a closed-loop self-adaptive regulation and control mechanism is constructed through residual distribution auditing, pseudo peak identification, gradient registration, differential entropy enhancement and time sequence threshold adjustment, fitting abnormity and false highlight spots in the infrared image are inhibited, the accuracy and stability of small target detection are improved, and the method is suitable for a complex photo-thermal environment.

The invention discloses a wildlife detection method fusing an unmanned aerial vehicle thermal infrared image and a visible light image, and belongs to the field of small target wildlife identification, and the method comprises the following steps: S1, obtaining a preprocessed TIR-RGB image pair set; s2, an FDM-YOLO double-source target detection model improved based on YOLOv81 is constructed, and the improved FDM-YOLO double-source target detection model is trained based on the preprocessed TIR-RGB image pair set obtained in the step S1; and S3, inputting an image acquired in real time into the improved FDM-YOLO double-source target detection model trained in the step S2, and outputting a wild animal detection result. By adopting the wild animal detection method fusing the thermal infrared image and the visible light image of the unmanned aerial vehicle, high-precision, real-time and robust detection of a small target of a wild animal in a complex field environment is realized by improving the FDM-YOLO model.

The invention discloses a lightweight multi-source unmanned aerial vehicle target detection method and system based on a DEYOLO framework, and relates to the field of target detection, and the method comprises the steps: obtaining an unmanned aerial vehicle visible light image and an unmanned aerial vehicle infrared image which are registered, and inputting the images into a pre-trained target detection model; the model comprises a double-flow feature extraction network module which is used for extracting an unmanned aerial vehicle visible light image and an unmanned aerial vehicle infrared image to obtain a visible light feature map and an infrared feature map; the bimodal adaptive feature weighting module is used for performing bimodal adaptive feature weighting and adding on the visible light feature pattern and the infrared feature pattern to obtain fusion features; the lightweight bimodal attention enhancement module is used for performing feature enhancement on the fusion features; and the detection head is used for detecting the enhanced features. According to the method, the calculation complexity is effectively reduced, and the detection precision and the reasoning speed of the model on the low-slow small target and the robustness of the model on a complex scene are remarkably improved.

The invention discloses a remote sensing image target detection method based on a Salience-DETR model, and the method comprises the steps: designing a multi-receptive field channel attention feature pyramid network (MRFCA-FPN) in a feature extraction stage, and effectively enhancing the detail perception and characterization capability of a model for a tiny target through a parallel multi-branch expansion convolution and channel attention adaptive fusion mechanism; a bidirectional adaptive fusion module (BAFM) is further introduced, an interaction path of deep semantics and shallow details is constructed by utilizing learnable scale alignment and bidirectional token fusion, and the focusing capability and positioning precision of a decoder to a small target area are remarkably improved; experiments on a VisDrone-DET2019 remote sensing data set and an AI-TOD remote sensing data set show that compared with a Salience DETR, the average precision (mAP at 0.5) of the improved SE-DETR model on the aspect of tiny target detection is improved by 3.1 percent points and 3.2 percent points respectively.

The invention relates to a robust unmanned aerial vehicle detection method based on dynamic feature fusion and context attention, and belongs to the technical field of image processing. Aiming at the problems of small target feature loss, semantic gap, background noise interference and the like caused by a fixed convolution kernel scale, one-way feature fusion and a static attention mechanism in an existing unmanned aerial vehicle aerial image target detection method, the method comprises the following steps: constructing a detection model comprising a backbone network, a neck network and a detection head network; a feature rearrangement and extraction module is designed in the backbone network to enhance feature learning, an enhanced double-flow feature fusion pyramid is designed in the neck network to optimize multi-scale feature fusion, and a dynamic multi-scale context attention mechanism is designed in the detection head network to suppress irrelevant background noise. The method effectively improves the accuracy and robustness of small target detection, and achieves a clearer and more stable detection effect in a complex environment.

The invention discloses a multi-scale SAR (Synthetic Aperture Radar) image ship detection method and system based on edge enhancement and diffusion denoising, and mainly solves the problems that the existing SAR ship detection method is sensitive to noise and poor in small target feature extraction capability. According to the implementation scheme, the method comprises the following steps: obtaining an SAR image, carrying out standardized preprocessing, inputting the SAR image into a deep convolutional neural network, extracting a multi-scale feature map, and carrying out dynamic channel fusion enhancement on a low-layer feature map in the multi-scale feature map to obtain a fused high-quality feature map; performing differential edge enhancement on middle and high-level feature maps in the multi-scale feature map, and inputting the enhanced feature map and the fused feature map into a diffusion model detection head for training; and inputting a to-be-detected SAR image into the trained diffusion model detection head, outputting a preliminary target bounding box and a category confidence coefficient, performing score screening and non-maximum suppression operation on the preliminary target bounding box and the category confidence coefficient, and generating a final ship target detection result. According to the method, the precision and robustness of SAR image ship detection are remarkably improved, and the method can be used for ocean monitoring and military reconnaissance.

The invention provides an infrared weak and small target detection method based on an adaptive sparse attention mechanism, and the method comprises the steps: collecting an infrared weak and small target image, carrying out the pre-screening through a bright and dark target balance module, and generating an infrared weak and small target data set; an infrared weak and small target detection model is constructed, the infrared weak and small target detection model is trained by using the infrared weak and small target data set, a trained infrared weak and small target detection model is obtained, and the model comprises an infrared image enhancement module, a linear embedding layer, an adaptive sparse attention Transform network and a low-rank approximate global feature fusion module which are connected in sequence; and inputting an infrared weak and small target image to be detected into the trained infrared weak and small target detection model to obtain an infrared weak and small target detection result. According to the method, the problem of insufficient detection precision of an existing method is solved, the robustness, the applicability and the real-time performance are high, and the visual perception capability of an infrared sensor platform to the surrounding environment is enhanced.

The invention discloses a low-slow small target detection and trajectory prediction tracking method based on a laser radar, and the method comprises the steps: firstly collecting the point cloud data of the laser radar, and carrying out the preprocessing of spatial modeling and coordinate transformation of the point cloud data of the laser radar; performing significance screening; based on distance partition driving, Pilllar construction and coding are carried out; constructing a deep learning detection network; based on Anchor design and a matching strategy, carrying out size adaptation on a weak target in the air in the fused features; and performing time sequence prediction and observation updating on the target state based on an extended Kalman filter (EKF), and completing low-slow small target detection and trajectory prediction tracking. The method can maintain the high precision advantage of the laser radar, improves the recognition capability of the laser radar on weak-reflection, small-size and irregular-motion targets, has high robustness and environment adaptability, and achieves the stable and precise sensing and continuous tracking of low, slow and small flight targets.

The invention discloses a dense small target detection method for an unmanned aerial vehicle aerial photography scene, and belongs to the technical field of computer vision and target detection. In order to solve the problems of small target scale dynamic change and feature expression weakening caused by flight height change, imaging resolution difference and scene complexity in aerial photography of an unmanned aerial vehicle, the invention provides a detection framework fusing an attention scale selection (AGSS) module and a dynamic local self-attention (DPSA) module. The method specifically comprises the following improvements: (1) an AGSS module enhances the significance and discrimination ability of small targets in multi-scale features through global context modeling and a dynamic weight distribution mechanism; and (2) a DPSA module introduces a sparse selection mechanism in a channel dimension, and focuses computing resources on a channel sensitive to a small target, so that efficient and lightweight attention modeling is realized. The above modules cooperate with each other, so that high reasoning efficiency is maintained, and small target detection precision and robustness in a complex background, low illumination and dense target scene are significantly improved. Experimental results show that on typical unmanned aerial vehicle aerial photography data sets such as VisDrone-DET2019 and the like, the method is superior to an existing mainstream method in multiple indexes such as the average precision (mAP), the accuracy rate and the recall rate, especially has obvious advantages in the aspects of integrity and stability of small target detection, and has good practical application value and popularization prospects.

The invention discloses a cigarette carton specification target detection method and system based on improved YOLOv11, and the method comprises the following steps: constructing a cigarette carton specification data set: collecting the images of different specifications of cigarette boxes in an automatic stereoscopic warehouse through an industrial camera, enhancing a few categories of samples by using a copying and pasting technology based on a specific region; an improved YOLOv11n model is adopted for training, the model comprises that in a backbone network and a feature fusion network, an ADown module is adopted to replace a standard convolution down-sampling module, the ADown module achieves feature fusion through a double-branch structure, and the feature fusion comprises a 3 * 3 convolution branch after channel segmentation and a maximum pooling + 1 * 1 convolution branch. According to the method, a visible cigarette carton specification data set is constructed, data enhancement and traditional enhancement based on specific area copying and pasting are carried out on the data set, and the obtained data set comprises factors such as complex backgrounds, shielding and overlapping, small targets and the like.

The invention discloses an irregular small target identification method under a non-high-definition complex background image. A multi-scale feature pyramid is constructed through bidirectional feature fusion, so that the feature expression ability of a small target is enhanced; applying a space-channel attention module to adaptively highlight target features and suppress complex background interference; by introducing a composite loss function including class balance focus loss and enhanced bounding box regression loss, model training is optimized to deal with class imbalance and improve the positioning precision of an irregular target. A self-adaptive multi-scale detection head is adopted, and dynamic feature fusion and scale perception branches are utilized to realize accurate detection of targets with different sizes; according to the method, the problems of low recognition precision and poor adaptability caused by weak features, background interference and irregular shapes of irregular small targets in low-resolution and complex background images are effectively solved, and the monitoring performance in actual applications such as unmanned aerial vehicle aerial photography and remote monitoring is remarkably improved.

The invention discloses an underwater target detection method and system, a storage medium and equipment, and relates to the technical field of target detection, and the method comprises the steps: obtaining an image data set of an underwater target; constructing an improved YOLOv8 model which comprises a backbone network, a neck network and a head network; a CA attention mechanism module is inserted into the backbone network; replacing a Neck neck network with a BiFPN neck network based on a feature pyramid, and replacing a CBS module with an ADSAMB module; and newly adding a detection head aiming at the tiny target in the head network. Using the preprocessed image data set to train the improved YOLOv8 model; and inputting a to-be-detected underwater image into the trained improved YOLOv8 model, and outputting a position bounding box, a category label and confidence of an underwater target to complete underwater target detection. According to the method, the accuracy, recall, mAP at 0.5 and mAP at 0.5-0.95 of the improved YOLOv8 model in a complex underwater environment are effectively improved, and the method is particularly excellent in the aspect of tiny target detection.

The invention discloses a wafer probe mark accurate detection method based on deep learning, and belongs to the field of wafer probe mark detection, and the method comprises the steps: constructing a pin mark image denoising preprocessing network, employing a small target feature protection and enhancement strategy based on HSV color space and local contrast joint adjustment, and carrying out the recognition of a small target feature; denoising and contrast optimization are carried out on the needle mark image; a multi-scene training sample is generated through mosaic splicing and mix fusion; a dense small target enhancement module is introduced into the backbone network to enhance needle mark feature expression, and a multi-scale feature fusion module is arranged in the neck network to extract full-scale features; and establishing an anchor frame optimization system adaptive to the minimum needle mark target, and adopting an optimizer and learning rate collaborative optimization training strategy to realize model adaptive convergence. According to the method, high-precision detection and robust identification of the wafer probe mark can be realized under a complex background, and the detection accuracy and stability are remarkably improved.

The invention discloses an offshore small target radar detection method and system, and relates to the technical field of radio detection. The method comprises the following steps: acquiring radar echo data, a visible light image, an infrared image, an AIS signal and navigation attitude data in real time; performing sea clutter suppression and pre-filtering on the radar echo data, and correcting target plots in combination with a historical radar plot set to obtain a radar single-source target track set; performing target detection on the visible light image and the infrared image to obtain an optical target detection set and an infrared target detection set; the radar single-source target track set, the optical target detection set, the infrared target detection set, the AIS signals and the navigation attitude data are associated and fused, and finally deception feature recognition and consistency verification are performed to obtain an updated fused target track set; according to the method, man-made confrontation and cheating behaviors on the sea can be accurately identified, and reliable support is provided for safety monitoring on the sea, maritime affair supervision and emergency response.

The invention relates to the technical field of image target detection, and discloses an unmanned aerial vehicle aerial image small target detection model construction method comprising the following steps: preparing an aerial image data set, preprocessing the aerial image data set, and generating an aerial image sample set; the method comprises the following steps: establishing a basic model on the basis of a YOLOv8 network model, removing a P5 detection layer in a head network Head of the basic model, introducing a P2 detection layer, replacing a specified position of a Conv module in a backbone network Backbone by adopting an ACMConv feature enhancement module, replacing a specified position of a C2f module in a neck network Neck by adopting a C2fMixStructure mixed structure module, and constructing an improved model by adopting a lightweight enhanced detection head structure; and dividing the aerial image sample set into a training set, a verification set and a test set in proportion, training and verifying the improved model, and generating an unmanned aerial vehicle aerial image small target detection model based on AMLP-YOLOv8. According to the invention, the method has higher perception capability when extracting fine target features, and improves the stability and robustness of small target detection of the aerial image of the unmanned aerial vehicle.

The invention discloses an unmanned aerial vehicle and bird target identification method and system based on multiple modes, and belongs to the technical field of computer vision and target identification. The method comprises the steps of collecting an RGB image, an infrared image and a continuous frame image of a monitoring area, extracting visual features, thermal radiation features and motion features after preprocessing, fusing multi-modal features by adopting an adaptive weight fusion strategy, detecting a potential target through a multi-scale feature fusion detection head, and obtaining a target detection result. The multi-mode classification module judges the target category and triggers early warning; the system comprises a data acquisition module, a preprocessing module, a feature extraction and identification module, a decision and early warning module and a database management module. Through multi-modal fusion, a self-adaptive weight strategy, multi-scale detection and multi-branch classification, the method can effectively overcome the limitation of a single modal, improves the recognition accuracy, the small target detection capability and the environmental adaptability in a complex environment, and meets the requirements of real-time recognition and early warning.

The invention relates to the technical field of remote sensing image change detection, in particular to a remote sensing image change recognition method and system fusing time sequence alignment and semantic perception, and the method comprises the steps: obtaining remote sensing images of different time phases in the same region, carrying out the multi-scale feature extraction, and achieving the feature alignment through optical flow estimation under the same scale, performing weighted fusion in combination with an attention mechanism to obtain a fused multi-scale feature group; further through multi-scale convolution and channel and spatial attention enhancement context and detail expression, generating a high-resolution feature map, and finally outputting a pixel-level change recognition map for indicating whether a corresponding geographic position is changed or not; by means of the synergistic effect of optical flow estimation and an attention mechanism, false changes caused by geometric displacement, shadow drifting and seasonal spectral difference can be effectively inhibited, and the stability and reliability of a detection result are improved; and meanwhile, under the support of multi-scale convolution and attention weighting, the recognition capability of the small target and the boundary region is enhanced.

The invention discloses a pyramid type YOLOv5 industrial small target detection system fusing an attention mechanism. The pyramid type YOLOv5 industrial small target detection system comprises the following steps: S1, data acquisition; s2, extracting features of the backbone network; s3, pyramid feature fusion is carried out; s4, fusing an attention mechanism; s5, detecting head prediction and bounding box regression; s6, performing multi-task loss synthesis and parameter updating; and S7, reasoning output and post-processing are carried out. The invention relates to the technical field of computer vision and industrial detection, and has the beneficial effects that the small target detection precision is improved: through introducing a pyramid feature fusion structure, shallow detail features and deep semantic features are effectively combined, the small target perception capability is enhanced, and the detection recall rate and the positioning precision are remarkably improved. And attention mechanism enhanced feature expression: fusing channel attention and space attention mechanisms, adaptively focusing on a key region and an important channel, inhibiting background interference, and improving the discrimination capability of the model for the industrial small target.