1678 results about "Contrast level" patented technology

The invention provides a circuit board detection method and system based on machine vision, and the method comprises the steps: obtaining an original visual data set of a to-be-detected circuit board, carrying out the visual information optimization processing of the original visual data set, and obtaining a standardized image set with unified illumination intensity and contrast, calling a pre-trained defect discrimination model to perform key feature recognition processing on the standardized image set, generating a potential defect feature set of the circuit board in the image, and determining defect types existing in the to-be-detected circuit board and position distribution feature information of defects in the image according to the potential defect feature set, and generating a detection result report containing defect positioning coordinates based on the defect type and the position distribution feature information, and outputting the detection result report to a target display terminal to complete the detection process. According to the invention, the accuracy of defect identification is improved, and the reliability of defect positioning is ensured in combination with the position marking information, so that the overall quality of a circuit board detection result is effectively improved.

The invention discloses a quartzite surface defect detection method, system and device, and relates to the technical field of image processing, and the method comprises the following steps: fixing a to-be-detected quartzite on a detection platform, and carrying out preprocessing; constructing a double-path polarization imaging light path; polarization parameter optimization is carried out based on the optical anisotropy characteristic of quartz stone, so that a polarization state difference is generated between a defect area and a normal area; under the condition of polarization parameter optimization, synchronously acquiring a first polarization image and a second polarization image through a double-path polarization imaging light path; performing polarization difference calculation on the first polarization image and the second polarization image to generate a polarization difference image; and carrying out contrast enhancement processing on the polarization difference image, identifying a defect area, carrying out defect classification, and outputting a quartz stone surface defect detection result. By optimizing polarization imaging parameter configuration and combining image processing, high-precision detection and intelligent classification of quartz stone surface defects are achieved, and the automation level and reliability of detection are improved.

The invention relates to the technical field of visual inspection, and particularly discloses an optical communication filter appearance defect detection method and related device.The optical communication filter appearance defect detection method comprises the steps that polarization image sets of an optical communication filter to be detected at different polarization angles are obtained; preprocessing the polarization image set to obtain a defect enhancement image set; performing defect detection on the defect enhancement image set based on a pre-trained multispectral attention fusion network; according to the method, multi-polarization information, special preprocessing and a deep learning network combined with frequency domain-space feature fusion and a cross-modal attention mechanism are utilized, the detection capacity of the defects which are tiny, low in contrast and interfered by film layer textures on an optical communication filter is improved, and the omission ratio is reduced.

The invention relates to the field of earth wall defect recognition, and discloses an assembled retaining wall defect recognition method and system based on image recognition, and the method comprises the steps: obtaining multi-view image data of an assembled retaining wall, and constructing a defect recognition image data set in combination with a boundary detail enhancement mechanism and a region illumination compensation strategy; performing edge guide feature extraction on the defect identification image data set, and constructing an image deep feature model based on a boundary context fusion network; judging whether the response intensity change of the image deep feature model in the crack region reaches a preset threshold value or not through an edge response enhancement mechanism, and if yes, marking that the crack region has potential defects; utilizing a multi-scale morphological structure analysis method to carry out contrast perception optimization on the corrected crack area; and based on a defect identification result, combining a component positioning mechanism and component historical operation and maintenance data to perform severity grading evaluation on the defect. The method has the advantage of improving the sensitivity to the marginal area.

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

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project crack detection method and system based on intelligent visual identification, and the method comprises the following steps: obtaining a crack image, enhancing the brightness and contrast in different regions, extracting and optimizing the crack edge, executing morphological processing, denoising and smoothing, segmenting a crack region, and judging the connection condition. And analyzing the geometrical characteristic detection connectivity, and calibrating the crack position to obtain a positioning result. According to the method, through dynamic adjustment of image brightness and contrast, the crack is more prominent in a complex background, especially under non-uniform illumination, crack identification difficulty caused by illumination inconsistency is reduced, crack edges are optimized through morphological operation, the missing part of the crack can be filled, the crack contour can be smoothed, and noise can be eliminated. And in the crack segmentation stage, through self-adaptive threshold and geometric feature analysis, the fracture and connection conditions of the crack are accurately judged, and positioning of the crack end point and the continuous area of the crack end point is achieved.

The invention relates to the technical field of image recognition, in particular to an image monitoring system for traditional village heritage risk assessment, and the system comprises a heritage image collection module which is used for obtaining an image data stream of a target traditional village building surface, carrying out the geometric correction of original heritage image data, carrying out the image brightness equalization processing, and obtaining an image data stream of the target traditional village building surface; and establishing a calibrated image set. According to the method, image distortion and local overexposure caused by shooting angle difference or uneven illumination on the surface of a traditional village building are eliminated through geometric correction and brightness equalization processing, and the input data quality of subsequent feature analysis is improved. The consistency degree of crack textures in a neighborhood range is quantified based on gray gradient direction field data, the gray contrast of continuous crack edges is enhanced in combination with a dynamic threshold adjustment mechanism, non-structural texture interference is inhibited, and separability of micro cracks and background materials is enhanced.

The invention discloses a method and a system for rapidly screening peanut endophytic mildew, particularly relates to the technical field of nondestructive testing of agricultural products, and is used for solving the problems of false detection and missing detection caused by low contrast and gradient boundary in an initial mildew stage and an internal diffusion sample in an existing detection method. Based on a hyperspectral imaging technology, a filtering kernel size is dynamically adjusted through conjoint analysis of frequency domain energy distribution and spatial local variance, and detail features of a gradient boundary in an image are enhanced; verifying and screening high-confidence candidate regions by adopting spectrum and spatial feature consistency, analyzing and extracting principal component features representing a mildew diffusion trend in combination with a texture direction, and dynamically splicing the principal component features with a spectrum gradient direction to construct a fusion feature vector; quantizing boundary confidence through a Bayesian probability classification model, combining with a neighborhood similarity constraint region growing algorithm to generate a mildewed mark graph with continuous space and consistent features, and introducing a dynamic threshold correction mechanism to adaptively optimize a segmentation standard; and the detection precision and the anti-interference capability of the fuzzy boundary are improved.

The invention discloses an underwater target detection method based on YOLOv8, and provides a corresponding solution for solving the problems of low contrast ratio, target imaging deformation, difficulty in detection of a small recognized target and the like in underwater optical image target detection so as to improve the underwater target detection precision. Firstly, a module with a receptive field attention mechanism is designed to be used for constructing a trunk feature extraction network, the multi-scale adaptive capacity of the model is improved from two aspects of receptive field range adjustment and feature randomness aggregation, and the robustness of the model to deformation target detection is improved; a smooth dynamic detection head is designed to replace an original detection head, and a multi-scale attention mechanism of the dynamic detection head and the smooth characteristic of a Softplus activation function are introduced, so that the characteristic response is more smoothly enhanced, and the detection performance of a fuzzy target is improved; finally, a WIS-IoU loss function is designed, quality evaluation is conducted on the anchor frame through a dynamic non-monotonic focusing mechanism of Wise-IoU, Shape-IoU considers shape and scale information of a target frame, then the concept of an Inner-IoU auxiliary bounding box is introduced, positioning precision and shape consistency are balanced, the model is evaluated more accurately, and training and optimization of the model are guided. The target detection network is more suitable for target detection in an underwater complex environment, and the underwater image target detection precision can be improved.

The invention relates to the technical field of image defect detection, in particular to an intelligent drug particle detection system and method based on image processing. According to the method, a high-resolution industrial camera and a polarized light source are used for dynamically shooting assembly line medicine particles so as to obtain multi-angle high-quality original images; noise suppression and microstructure enhancement are realized through multi-scale residual information enhancement and structure maintenance adaptive filtering, and the detail resolution is improved by using local contrast normalization; a multi-scale structure gradient of direction perception and region shape prior are fused, and a hierarchical contour evolution and dynamic threshold strategy is adopted to accurately segment a particle main contour and a microcrack; micro defects are identified based on a multi-scale texture sensing algorithm guided by a boundary relative potential graph, and multi-class defect classification is realized through a spatial and semantic dependency relationship between graph neural network modeling regions; and finally, a traceable quality detection report is generated. The intelligent level, the accuracy level and the automation level of medicine particle detection are improved.

The invention relates to the technical field of image recognition, in particular to an intelligent electric energy meter image collection method and system based on multi-source data, and the method comprises the following steps: collecting a multi-angle reflection contour image of an electric energy meter through a preset incident angle light source, and calculating a difference value point by point based on a standard reference contour point coordinate; and after edge sections are divided, the maximum value, the minimum value and the mean value are processed by adopting a Gaussian filtering algorithm, and a multi-angle difference statistical result is generated. According to the invention, through multi-angle reflection contour image acquisition, difference calculation and edge division, Gaussian filtering noise reduction, ultralimit edge segment extraction, angle focal length compensation through step length adjustment, edge extraction through a Canny edge detection algorithm and contrast change rate calculation, dynamic path instruction generation, high-frequency feature point density extraction through SIFT, and direction angle adjustment scanning sequence analysis, the method can be used for realizing multi-angle detection of the image. Calculating a standard deviation and a gradient variance to generate an abnormal graph, performing local focusing to compensate an abnormal region, performing gray scale consistency splicing replacement, and performing multi-source response optimization closed-loop correction.

The invention discloses a black pig image segmentation method based on multi-feature fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining a black pig image, and carrying out the contrast enhancement processing; inputting the enhanced image into a residual convolutional network to generate a depth feature map, extracting a local shape feature map through curvature threshold segmentation and a double fitting strategy, and fusing the two feature maps to generate a black pig feature map; establishing a spatial position prior probability graph based on the black pig sample library, calculating regional correlation and performing adaptive weighting to obtain a fusion feature graph; boundary segmentation and iterative optimization are carried out on the fused feature map based on the dynamic behavior pattern map and the attitude constraint rule, and an initial segmentation map is generated; and adopting a group behavior model as an optimization criterion, correcting the boundary of the initial segmentation image, and outputting a final segmentation result. According to the method, the segmented enhancement function based on the double-peak characteristic and the local texture feature self-adaptive adjustment strategy are constructed, so that differential enhancement of image preprocessing is realized.

The invention relates to the technical field of image processing, in particular to an image segmentation system for medical diagnosis, which comprises a regional characteristic analysis module, a modal selection optimization module, an image local enhancement module, an edge characteristic analysis module and a boundary optimization adjustment module. According to the medical image segmentation method, the gray level distribution, the texture density and the structure contour of the tissue structure in the image layer are analyzed, the system is allowed to accurately measure the characteristic deviation between different modes, the accuracy of medical image segmentation is improved, especially on the processing of the complex tissue structure, the adjacent tissues with different properties can be better analyzed and distinguished, and the medical image segmentation accuracy is improved. Real-time evaluation and adjustment of local contrast enable details of the image to be clearer, image quality is improved, accuracy of edge recognition is improved, by analyzing edge curvature and morphological characteristics, the system can optimize edge trend and reconstruct a boundary path, overall performance of image segmentation is further improved, and image segmentation efficiency is improved. Therefore, the treatment and prognosis effects of the patient are ensured.

The invention belongs to the technical field of traffic safety monitoring technologies, and discloses a defogging method and equipment based on infrared light and visible light image fusion. The method comprises the following steps: image preprocessing: denoising and enhancing a foggy image; inputting the preprocessed picture into a defogging model based on infrared light and visible light image fusion to obtain a defogged image; the defogging model based on infrared light and visible light image fusion carries out the following processing on an input picture: processing an infrared light image, enhancing the penetration effect of the infrared light image in a fog environment, and highlighting the contour information of a target; and processing the visible light image, and recovering the color and texture details of the visible light image in the fog environment. According to the method, the definition and the visual effect of the foggy day image can be remarkably improved, the target is prominent, the color is natural, the contrast ratio is effectively improved, the detail information of the image is reserved, rapid defogging processing is realized, and the accuracy of fog concentration estimation is enhanced.

The invention discloses a real estate information verification method and system based on an image technology, and relates to the technical field of real estate information verification, and the method comprises the following steps: image collection: employing a collection device with multiple functions, and through the means of adjustable illumination, automatic paper flattening, multi-mode scanning, real-time quality monitoring, etc. Obtaining a high-quality and comprehensive paper house property certificate file image; acquiring a paper house property certificate file image by utilizing acquisition equipment which is provided with an adjustable lighting system and has an automatic paper flattening function; according to the method, comprehensive information is acquired through multi-mode scanning, the risk of information omission is effectively reduced, noise is accurately removed through image preprocessing, the contrast ratio is enhanced, information missing is repaired, a clear image is provided for follow-up processing, multiple kinds of accurate feature analysis are newly added in the feature extraction link, rich and accurate feature vectors are formed, and the image quality is improved. And the capability of judging the authenticity and integrity of the file is greatly enhanced, so that the accuracy and reliability of verification are remarkably improved.

According to the multi-mode visual stimulation and intelligent self-adaption combined myopia prevention and control system based on the AR glasses, all-weather, personalized and full-scene myopia suppression is realized through multi-module cooperation of sensory visual stimulation, visual function training, AI scene dynamic recognition and directional intervention, self-adaption closed-loop feedback, cloud / offline deployment and the like; through multi-dimensional visual stimulation and real-time physiological monitoring, eye axis growth is accurately regulated and controlled, myopia prevention and control precision is improved, out-of-focus and contrast setting are automatically optimized based on immediate refraction and adjustment feedback, personalized intervention is enhanced, lightweight AI and cloud pre-calculation are introduced, the endurance of equipment is effectively prolonged, an existing AR platform is compatible, the development process is simplified, and the development efficiency is improved. And the user compliance is enhanced.

The invention belongs to the field of computer vision and industrial defect detection, and discloses a steel coil end face defect detection method and system based on CBAM-BiFPN and multi-loss optimization, and the method comprises the steps: image preprocessing and region extraction: extracting a steel coil end face region through OpenCV and other image processing methods; dividing the high-resolution image into a plurality of small blocks and performing data enhancement; the method comprises the following steps: constructing a YOLOv11 network based on CBAM-BiFPN, introducing a CBAM attention mechanism and BiFPN feature fusion structure, and constructing an improved YOLOv11 detection network; multi-loss function joint optimization training is carried out, and model training is carried out in combination with loss functions such as Focal Loss and WIoU; and fusion of detection results and defect reconstruction output: reconstructing original image defects of all tile image detection results in a space coordinate mapping and redundant region fusion mode, and realizing high-precision overall detection output. Compared with a traditional method, the method still has the high recognition capability in a complex background and low-contrast scene, the detection precision and stability are obviously improved, and higher industrial adaptability and practical value are achieved.

The invention relates to the technical field of road defect detection, in particular to a road pit detection method and system, and the method comprises the following steps: S1, obtaining an original image flow of a road surface through a vehicle-mounted multispectral camera, and carrying out the motion artifact elimination to generate a vibration compensation image; s2, generating a shadow suppression image through asymmetric gamma correction; s3, adopting a dual-threshold connected domain analysis method to extract candidate pothole regions; s4, when the contrast difference value exceeds a texture mutation threshold value, determining that the area is a surface damaged area; s5, extracting a continuous pixel cluster statistical area proportion, and when the proportion is greater than 60%, determining that the feature is an effective pothole feature; and S6, calculating the minimum enclosing ellipse eccentricity rate and the ellipse area ratio, and generating a final pothole detection report. According to the method, through a multi-source information fusion and multi-stage feature extraction method, high-precision identification and standardized output of the pothole area in a complex road environment are realized, and the detection accuracy and the application reliability are improved.

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

The invention discloses a multispectral image fusion model based on a double-branch self-attention-generative adversarial network and a fusion method thereof, and the model sequentially comprises an input preprocessing module which is used for carrying out the same-amplitude mapping, normalization and overlapping block embedding of visible light and infrared original images, and generating a to-be-fused feature block; the double-branch encoder module captures a cross-modal long-distance dependence and overall brightness structure through multi-head deep convolution transpose attention (MDTA) and a gated deep convolution feedforward network (GDFN), and extracts high-frequency texture and edge information by using a reversible residual gating layer and detail DetailNode iteration; the fusion decoder module is used for carrying out multi-level self-attention-convolution reconstruction on the two paths of features after channel dimension splicing, and outputting a single-frame high-resolution fusion image; and the double-domain discriminator module comprises a visible light domain discriminator and an infrared domain discriminator which are respectively used for carrying out adversarial evaluation on the fused image and the corresponding modal truth value image so as to improve the detail authenticity and the thermal target contrast ratio of the fusion result. The technical problems that an existing infrared-visible light image fusion method is insufficient in detail reservation, unbalanced in brightness and contrast, poor in unsupervised training stability and the like are solved, the method can be deployed on embedded platforms needing real-time and multi-modal information enhancement such as night monitoring, unmanned driving and edge security and protection, and high-contrast and high-information-amount fusion imaging is achieved.

The invention provides a numerical control machining defect real-time detection method and system based on AI image recognition and a medium, and belongs to the technical field of intelligent manufacturing and machine vision crossing. The method comprises the following steps: collecting part surface image data according to a preset frequency, and separating a part from a background through noise reduction, contrast enhancement and a dynamic threshold algorithm of a data preprocessing module; inputting the processed image into a multi-modal feature fused AI detection model, and extracting and fusing multi-modal features to judge the type and position of a defect; and processing the image by using a parallel computing architecture, feeding back a detection result to a numerical control processing control system in real time, and visually displaying the detection result on a monitoring interface. According to the method, real-time and accurate detection and feedback control of numerical control machining defects are realized, and the machining quality and efficiency are effectively improved.

The invention relates to the technical field of intelligent spectrum tuning, in particular to an intelligent spectrum tuning code scanning auxiliary lighting method, system and device and a medium. The method comprises the following steps: firstly, monitoring environment illumination intensity, color temperature and a light incident angle in real time through an illumination sensor, and meanwhile, detecting a spatial position relationship between a two-dimensional code and equipment by utilizing a distance sensor; the system performs intelligent analysis according to the real-time parameters, determines an optimal light supplementing strategy, and controls the RGBW LED array to output a corresponding spectrum combination; the system continuously monitors the scanning effect after light supplementation, and dynamically adjusts light supplementation parameters by evaluating the definition and contrast of the two-dimensional code until the optimal scanning state is reached; the environment illumination parameters and the spatial position parameters are combined for comprehensive analysis, and the RGBW LED array is adopted to realize adjustable spectrum output, so that the influence of the outdoor complex illumination environment on two-dimensional code scanning can be effectively overcome, and the payment efficiency is remarkably improved.

The invention discloses a micro-channel aluminum flat tube appearance detection method based on image processing, and relates to the technical field of appearance detection.According to the method, more comprehensive information acquisition is carried out through the multispectral imaging technology in combination with RGB, polarized light, infrared spectrum images and ultraviolet spectrum images, and surface temperature difference changes are detected through infrared spectrums; image preprocessing is carried out through contrast-limited adaptive histogram equalization and bilateral filtering, uneven illumination is inhibited while edge details are kept, the contrast of corrosion defects is improved, details of a low-resolution area are enhanced through a super-resolution reconstruction technology, edge features of a corrosion area are further optimized in combination with a histogram in the gradient direction, and the edge features of the corrosion area are further optimized. The identification capability of small-scale defects is improved; an improved YOLOv3 defect detection algorithm is introduced, multi-scale feature extraction is adopted, and a self-attention mechanism is introduced, so that the model pays more attention to a tiny corrosion area, and meanwhile, the detection precision of a small target is enhanced in combination with focus loss and regression loss.

The invention discloses a product defect detection method and system based on multispectral imaging, and the method comprises the steps: obtaining multispectral image data of a financial payment terminal product, and generating a first image set; performing de-noising filtering and contrast enhancement processing on the first image set to generate a second image set; performing feature extraction based on the second image set to generate a feature set; and constructing a special convolutional neural network model of the plastic shell and the metal circuit board according to the feature set, generating a model set adaptive to material difference through transfer learning, and determining a final detection model to perform defect detection on the real-time multispectral image to obtain a detection result. The accuracy and efficiency of financial payment terminal product quality detection are improved, data support is provided for production process optimization, the product defect rate is effectively reduced, and the product quality and reliability are improved.

The invention provides an infrared thermal imaging image processing method for a pain area, and relates to the field of image enhancement, and the method combines multi-scale adaptive contrast enhancement and a bidirectional circulation mechanism, captures global and local temperature features of an infrared image, constructs an iterative feature enhancement module through an incremental fusion strategy, and carries out the image enhancement through the iterative feature enhancement module. Different level features are fused in stages, learnable parameters are introduced to dynamically adjust the new and old feature fusion proportion, the adaptability and flexibility of the model are improved, and a staged training strategy is adopted, so that the model can learn low-level thermal image information and also can extract high-level structures and thermal anomaly semantic features. According to the method, the adaptability of the model to different types of focus areas is enhanced, and the actual demand of efficient and automatic processing of the infrared image is met.

The invention discloses a multi-modal fusion defect detection method and system, and belongs to the technical field of intelligent detection and machine vision, and the system comprises a visible light sensor, a thermal infrared sensor, a hyperspectral sensor, a multi-band light source trigger control system, a modal preprocessing and alignment module, a cross-modal feature fusion module, and a defect detection and output module. According to the invention, three sensors are used to construct a multi-modal visual perception system, and a multi-band light source triggers a control system to complete image acquisition; after multi-modal information is subjected to preprocessing and cross-modal alignment through the modal preprocessing and alignment module, multi-modal fusion is achieved through the cross-modal attention module and the multi-scale feature fusion pyramid structure, and finally defect recognition and output are conducted through the defect detection and output module. According to the method, the defect identification precision can be obviously improved, and the method has obvious advantages especially for low-contrast and early-stage hidden crack defects, and has good expandability and deployment suitability at the same time.

The invention relates to the technical field of defect detection, and discloses an industrial surface defect detection method, system and device and a storage medium. The method comprises the following steps: receiving an input surface image of a to-be-detected industrial product and carrying out image preprocessing to obtain a preprocessed surface image; performing multi-scale decomposition on the preprocessed surface image to obtain images of different scales; processing the images of different scales based on an adaptive local contrast enhancement algorithm to obtain enhanced images of different scales; performing multi-scale fusion on the enhanced images of different scales to obtain a target enhanced image; and performing defect detection on the target enhanced image based on an adaptive threshold segmentation method to obtain a defect candidate region. According to the method, industrial surface defect detection is carried out in a mode of combining multi-scale analysis and self-adaptive local contrast enhancement, so that the accuracy and robustness of defect detection are effectively improved, and the requirement of real-time detection in industrial production can be met.

The invention relates to an improved YOLO11n underwater target identification and detection method based on local and global perception, and belongs to the field of underwater intelligent identification. According to the method, a local bottleneck module is constructed to extract local fine-grained features, a multi-scale large convolution kernel module is introduced to expand a receptive field, and a multi-branch channel attention module, a space and channel combination grouping attention module and a cross-dimension feature fusion module are combined to adaptively enhance effective features and suppress environmental noise interference. And a local-global bottleneck module is further constructed, and local detail and global semantic collaborative modeling is realized through a cascade structure. And a Bottleneck module of the original YOLO11n is replaced by the module, so that an improved model is formed. After training, the method has higher detection precision and robustness on underwater data sets such as DUO, RUOD and the like, and is suitable for real-time target identification tasks in a complex underwater environment. According to the method, the problems of color distortion, low contrast, fuzzy details and difficulty in multi-scale target feature extraction of the underwater image are solved, and high-precision and real-time balance detection is realized.

A data-centric system for analyzing agricultural crops, consisting of: a data acquisition module configured to capture images of agricultural fields using cameras, unmanned aerial vehicles (UAVs) or sensors, with the sensors collecting data on soil moisture, temperature, light, humidity and pH; a data preprocessing module configured to: resize the acquired images to a standardized dimension suitable for input to a deep learning model; apply noise reduction using a Gaussian filter; improve image contrast through histogram equalization; and perform image magnification through rotation, reflection, and scaling transformations; a feature engineering module configured to extract the following features: color features, which include color histograms, mean, and standard deviation of color channels; texture features using Gray-Level Co-occurrence Matrix (GLCM) properties, which include contrast, dissimilarity, homogeneity, energy, angular moment (ASM), and correlation; shape features, which include contour area, perimeter, aspect ratio, and roundness; and other features, which include the green pixel ratio and edge density; a classification module configured to: implement deep learning-based classification models selected from the group consisting of Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), ResNet18, Random Forest (RF), SegNet, VGGNet, Naive Bayes (NBG), Decision Tree (DT), K-Nearest Neighbors (KNN), and DeepLab; detecting and classifying weed species in the images of agricultural fields; and diagnosing plant diseases based on the features extracted from the images of agricultural fields; an output module comprises a user interface configured to display the classification and recognition results; and a recommendation module configured to suggest treatment solutions for diagnosed plant diseases through the output module's user interface.

The invention discloses a liquid medicine foreign matter detection method and system based on machine vision, and the method comprises the steps: obtaining multiband spectral data of a liquid medicine sample, and carrying out the normalization processing to generate a liquid medicine background standard spectral feature template; comparing the refractive index spectral characteristics of the template and the foreign matter-containing liquid medicine, and recording the surface roughness scattering mode and polarized light reflection angle change data of a foreign matter area; calculating a spectrum peak displacement quantized value according to the polarized light data, and extracting foreign matter edge spectrum gradient information in combination with a threshold value; calculating a transparency spectral attenuation coefficient based on the edge gradient information, and obtaining spectral contrast enhancement factors of different foreign matters in combination with the spectral response characteristic data; and if the enhancement factor does not reach the standard, adjusting the spectrum correction parameter and recalculating, fusing the scattering mode and the multi-angle incident spectrum response result to carry out consistency verification, and obtaining a final identification result containing the foreign matter material type and the danger level. The method can improve the drug detection accuracy and guarantee the drug quality safety.