189 results about "Adaptive histogram equalization" patented technology

The invention discloses an unmanned aerial vehicle electric power inspection image intelligent analysis method and system based on deep learning and multi-modal fusion and a medium thereof, and relates to the technical field of electric power equipment detection. The method comprises the following steps: planning an optimal inspection path by adopting an A * algorithm to realize multi-sensor synchronous data acquisition; adaptive histogram equalization and defogging processing are carried out on the visible light image, non-uniformity correction and temperature calibration are carried out on the infrared image, and filtering and registration are carried out on point cloud data; constructing a multi-scale feature fusion network based on improved VGGNet-16, and introducing deformable convolution and a cross-modal attention mechanism to realize multi-source data fusion; defect detection is carried out based on a three-level template library and a feature map cross-correlation algorithm, and the precision is improved in combination with non-maximum suppression and sub-pixel positioning; and finally generating a detection report containing defect types, positions and maintenance suggestions. According to the invention, the automation level and the detection precision of power inspection are obviously improved.

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The invention relates to the technical field of image enhancement, in particular to a protector bimetallic strip detection method based on machine vision, and the method comprises the steps: obtaining a surface image of a bimetallic strip, and carrying out the preprocessing of the surface image, and obtaining a gray image; performing threshold segmentation on the grayscale image to obtain at least one segmentation region; obtaining an evaluation coefficient of each segmented region, obtaining an adaptive cutting parameter when contrast-limited adaptive histogram equalization is carried out on each segmented region according to the evaluation coefficient of each segmented region, and carrying out image enhancement on each segmented region in the grayscale image according to the adaptive cutting parameter to obtain an enhanced grayscale image; the corrosion detection result in the bimetallic strip is obtained by using the enhanced gray level image, so that the enhanced image can reflect more effective information, and the surface corrosion detection precision of the bimetallic strip according to the enhanced image is improved.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

The invention relates to a segmentation model based on a CNN and Transform parallel double-branch structure, and belongs to the technical field of medical data prediction. The method comprises the following steps: acquiring a CT image, and preprocessing the CT image by executing windowing processing and contrast limited adaptive histogram equalization; extracting features of lung lesions in the preprocessed CT image through a parallel double-branch structure; inputting the extracted features into a cross enhancement fusion module, and performing complementary fusion on the features through dynamic weight distribution to obtain fused features; the fusion features are input into a multi-scale context information extraction module, and lesion boundary sensitivity is enhanced through cavity convolution of different expansion rates; the encoder features and the decoder features are fused through jump connection, and a segmentation result is output after resolution is recovered based on up-sampling; and optimizing model training by adopting a weighted loss function. Accurate segmentation of the lung lesion in the pulmonary tuberculosis CT image is realized, and clearer and more accurate lesion area information can be provided.

The invention discloses a carbon-coated foil coating uniformity detection method and system based on machine vision, and the method comprises the steps: S1, collecting blue light, green light, red light and near infrared spectrum images of the surface of a carbon-coated foil coating through a multi-angle camera, and generating a fused composite image through a multispectral image fusion algorithm; s2, performing adaptive histogram equalization and reflected light spot suppression processing on the fused composite image, and outputting an enhanced image; s3, coating edge features in the enhanced image are extracted, segmentation of a coating region is realized in combination with a region growing method, and a coating region mask is obtained; s4, extracting multi-scale texture features of the coating region by adopting a pre-trained ResNet-50 deep convolutional network, and constructing a coating surface feature vector; and S5, based on the extracted feature vectors and a standard template library, through a uniformity evaluation model, calculating a uniformity index of the coating, and detecting the uniformity of the coating. According to the invention, accurate detection of the coating uniformity can be realized.

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 multi-scale feature detail enhanced lithium battery surface defect detection method and system, and relates to the technical field of defect detection, and the method comprises the steps: carrying out the lithium battery region segmentation of a to-be-detected lithium battery surface image, and obtaining a lithium battery region image; performing pyramid decomposition and adaptive contrast limit adaptive histogram equalization based on the lithium battery region image through a multi-scale adaptive enhancement module, and outputting a plurality of scale feature maps; respectively inputting each scale feature map into a comprehensive enhancement module for detail enhancement, and determining a corresponding detail feature map; performing up-sampling fusion on each detail feature map by adopting a feature fusion module to generate an enhanced lithium battery region image; and performing defect detection on the enhanced lithium battery area image based on the target detection model, and outputting a detection result. Based on the scheme, the defect detectability is improved through enhancement treatment, and the lithium battery surface defect detection reliability can be improved.

The invention belongs to the technical field of image processing, and particularly relates to a monitoring image defogging enhancement method in a high-dust environment of a mineral separation site, which comprises the following steps of: performing smooth denoising on an original image by using weighted guided filtering, and obtaining global atmospheric light through a quadtree subdivision method; constructing a same-color heterogeneous discrimination index combining a spectrum similarity factor and a texture confidence factor for distinguishing dust and ore with similar colors; calculating a pixel-level dynamic defogging coefficient based on the discriminant index, and obtaining an adaptive transmissivity in combination with dark channel prior; and finally, restoring the image by using an atmospheric scattering model and carrying out contrast-limited adaptive histogram equalization processing. According to the method, the problem of misjudgment caused by the fact that the colors of the ore and the dust on the ore dressing site are similar is effectively solved, powerful defogging of the dust area and detail reservation of the ore area are achieved, and the definition and the contrast ratio of the monitoring image are improved.

The invention relates to the technical field of underwater image enhancement, in particular to an underwater image enhancement method based on balance, correction and deblurring and application. The balance module is cascaded by color balance and illumination balance, and the color balance performs compensation balance on RGB (Red Green Blue) color channels of the underwater image based on a white balance algorithm of red channel compensation; according to illumination balance, an improved contrast-limited adaptive histogram enhancement method is used for enhancing illumination of a low-illumination area of an underwater image, and meanwhile illumination of a high-illumination area is restrained, so that illumination balance is achieved. In the correction module, a color and illumination composite correction method combining color constancy and an illumination diagram is provided so as to correct the color and illumination of the underwater image at the same time. And in the deblurring module, the image is deblurred by adopting a multi-scale anti-sharpening mask, so that the definition of the underwater image is improved.

The invention relates to the field of hydrological monitoring, in particular to a hydrological monitoring early warning method and system based on an unmanned aerial vehicle. The method comprises the following steps: acquiring a panoramic continuous aerial image of a to-be-monitored area; adaptive histogram equalization and image time delay interpolation optimization are carried out on the panoramic continuous aerial image of the to-be-monitored area to construct a time delay optimized continuous image; performing water body landform semantic analysis on the time delay optimized continuous image, performing real-time three-dimensional landform modeling, and constructing a real-time three-dimensional landform model; performing continuous frame optical flow tracking on the time delay optimized continuous image, performing inter-frame optical flow motion vector calculation, and generating a water level fluctuation curve and surface ponding area change characteristics; and carrying out instant hydrological state rendering on the real-time three-dimensional terrain model according to the water level fluctuation curve and the surface ponding area change characteristics so as to construct a dynamic hydrological state evolution model. According to the invention, accurate and efficient hydrological monitoring and early warning are realized, and the safety of the hydrological basin is improved.

The invention relates to the technical field of computer vision, and discloses a jade defect intelligent detection method and system based on machine vision and deep learning, and the method comprises the following steps: S1, based on a high-resolution industrial camera and a laser three-dimensional scanner, adopting a multi-mode synchronous collection strategy, and rotating a jade sample through a precise motion control system, a jade surface high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are respectively obtained, and a jade multi-mode original data set is generated. A high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are integrated through a multi-modal synchronous acquisition strategy, and multi-dimensional feature expression under unified coordinates is constructed, so that the limitation of a single data source is effectively overcome; an image registration algorithm and a feature pyramid network are combined with a point cloud network to perform multi-modal feature fusion, complementarity of color texture and geometric morphology information is enhanced, and image quality is optimized based on adaptive histogram equalization and non-local mean filtering.

The invention provides a real-time processing method for endoscopic image blood vessel enhancement and microcirculation evaluation, which comprises the following steps of: processing an original endoscopic image, and locally enhancing a brightness channel through a contrast-limited adaptive histogram equalization technology; self-adaptive frequency domain-space domain decomposition is realized based on local texture complexity analysis; performing multi-scale Hessian matrix blood vessel detection on the low-frequency component; applying a directional Gabor filter bank to the high-frequency component; spatial-temporal feature fusion is realized through multi-resolution pyramid optical flow calculation; synchronously completing blood vessel probability prediction, blood vessel diameter estimation and blood flow direction prediction by using a lightweight multi-task deep learning network; the blood flow velocity is analyzed and calculated based on a speckle mode, the perfusion density is subjected to accelerated statistics through an integrogram, and vascular morphological parameters are extracted by adopting an improved skeleton algorithm. According to the invention, an enhanced blood vessel visualization effect and a real-time microcirculation quantitative evaluation function can be provided, and the overall improvement of the endoscope image processing quality and efficiency is realized.

The invention relates to the technical field of image processing, and discloses a gynecological cell morphology intelligent identification method and system based on machine vision, and the method comprises the steps: collecting an original image of gynecological cells, carrying out the color normalization processing of the collected original image, carrying out the denoising processing through employing a wavelet threshold value, and carrying out the image enhancement through employing adaptive histogram equalization; according to the method, color consistency errors of different batches of dyed images are reduced, noise introduced in the image acquisition process is eliminated, cell structure details are reserved, and the accuracy of diagnosis is improved. And then image enhancement is carried out, the contrast ratio of cell nucleuses and cytoplasm is improved, so that the characteristics in the cells are more obvious, segmentation can be more accurately completed during subsequent image segmentation, the lesion level of the gynecological cells can be accurately identified, and the accuracy of an identification result is ensured.

The invention relates to a balanced color perception enhancement method for rail transit target detection, and belongs to the technical field of rail transit and computer vision. According to the method, brightness channel adaptive histogram equalization enhancement CLAHE-LC, a multi-segment tone channel mask mechanism MSHCM-M and a three-stage hybrid mechanism non-maximum suppression TSLSH-NMS technology are combined, so that the problems of complex illumination, multi-target shielding, color interference and the like in a rail transit scene are solved, and the accuracy and recall rate of target detection are improved. The method can be seamlessly integrated into an existing deep learning target detection framework, is compatible with multi-label, multi-category and multi-scale features, and remarkably improves the precision and recall rate of target detection in a rail transit scene. Experimental verification shows that the real-time performance is guaranteed, meanwhile, the detection performance is obviously improved compared with a traditional scheme, and the method is suitable for being applied to an actual rail transit safety monitoring and intelligent maintenance system.

The invention relates to the field of tea disease detection, in particular to a tea disease detection method based on a lightweight YOLOv8 model. According to the technical scheme, the method comprises the steps of collecting various types of tea disease images; the method comprises the following steps: preprocessing multiple types of collected tea disease images, including illumination normalization, geometric enhancement, illumination enhancement, adaptive histogram equalization, a multi-scale Retinex algorithm, dynamic Gamma correction and image noise removal, and marking scab regions in different types of tea disease images to obtain a training data set; a lightweight YOLOv8 model is constructed; using the training data set to train the lightweight YOLOv8 model; and inputting a collected tea disease image into the trained lightweight YOLOv8 model, and outputting a tea disease type through the trained lightweight YOLOv8 model. The method is suitable for tea disease detection.

The invention discloses an SLAM front-end optimization method based on brightness grading and gradient constraint, and relates to the technical field of computer vision and vision SLAM. In order to solve the defects that adaptive grading enhancement and parameterization control based on illumination types are lacked in the prior art, and feature point quality, spatial distribution uniformity and front-end real-time performance are difficult to consider in a high-frequency input scene, the invention provides a comprehensive brightness grading and feature optimization scheme. The method comprises the following steps: firstly, calculating the average brightness of an input image, dividing the image into a dark light type, a normal type and an overexposure type, and respectively adopting gamma correction, contrast limited adaptive histogram equalization and inversion enhancement strategies for different types to realize illumination adaptive enhancement; then screening high-quality feature points with significant local curvature changes; and updating the detection area. According to the method, the feature stability, the matching precision and the real-time performance of the SLAM front end in a complex indoor environment are remarkably improved, and the method is suitable for a self-localization and mapping system.

The invention discloses an underwater image enhancement method and system. The method comprises the following steps: performing L-channel contrast enhancement on an original underwater image to obtain a local enhanced image; performing color recovery on the original underwater image by using a contrast-limited adaptive histogram equalization method to obtain an equalized image; performing linear weighted fusion on the locally enhanced image and the equalized image to obtain a fused image; performing color correction on a # imgabs0 # channel and a # imgabs1 # channel on the fused image to obtain a corrected image; performing depth prediction on the fused image through a monocular depth estimation method to obtain a depth guide map; carrying out RGB channel information expansion on the fused image to obtain a detail image; and carrying out image fusion on the corrected image, the depth guide image and the detail image to obtain a final enhanced image. According to the method, the coordination and fusion of local contrast enhancement and global color recovery are realized, so that the visual quality of the enhanced image is remarkably improved.

The invention discloses a pavement crack image segmentation method based on a convolution block attention and diffusion model, and belongs to the technical field of computer vision deep learning application. The problem of excessive enhancement caused by traditional histogram equalization is solved through an adaptive histogram equalization algorithm, the contrast of a crack region is remarkably improved while texture details are reserved, and a higher-quality data basis is provided for subsequent segmentation; a segmentation framework combining a convolution block attention mechanism and a diffusion generation model is adopted, crack features are accurately positioned through a space-channel double attention mechanism, interference of road background noise is effectively inhibited, and the crack segmentation accuracy is improved; a feature-level joint training mechanism is adopted, convolution attention enhancement of an FEF module is matched, multi-dimensional feature complementation is achieved, multi-scale features are effectively combined, space positioning of a crack area is enhanced, an end-to-end model is constructed, and convenience of image segmentation is enhanced.

The invention discloses an unmanned aerial vehicle small target detection method based on Mama feature fusion, and relates to the technical field of computer vision, and the method comprises the steps: multi-modal data collection, synchronous collection of images and point cloud data through three types of sensors, and coverage of multi-scene and environment conditions; image preprocessing adopts improved bilateral filtering, adaptive histogram equalization and point cloud downsampling to unify a coordinate system; in the multi-scale feature extraction, five-level scale features are output through an improved CSPDarknet network, and the five-level scale features are enhanced through an SENet attention module; in the Mama feature fusion, multi-modal and multi-scale features are processed through a three-stage unit; generating a candidate frame by a dynamic anchor frame, screening according to IOU, and improving YOLOHead to realize classification and positioning; the dynamic optimization of the detection result finely adjusts parameters through on-line distillation. According to the method, the precision, the real-time performance and the anti-interference capability of small target detection of the unmanned aerial vehicle are improved, and reliable technical support is provided for low-altitude security, exploration and other scenes.

The invention relates to the field of image processing, in particular to an electric cylinder piston rod surface defect detection method and system, and the method comprises the steps: carrying out the preprocessing of an obtained piston rod surface image, and obtaining a gray-scale image; equally dividing the grey-scale map to obtain a plurality of image blocks, calculating cutting thresholds of the image blocks, performing adaptive histogram equalization according to the cutting thresholds, and merging all the image blocks to obtain an enhanced image; and inputting the enhanced image into a preset defect detection model, and outputting a defect detection result. The image enhancement effect can be improved.

The invention provides a chromosome image enhancement method and system based on semantic guidance. The method comprises the following steps: S1, preprocessing; s2, outputting a deep-band probability graph, a grey-band probability graph and a shallow-band probability graph through a semantic segmentation network composed of a lightweight encoder and a multi-scale decoder; s3, implementing differential layered enhancement according to a band type: adopting local adaptive histogram equalization for a deep band, adopting central axis constraint bilateral filtering for a gray band, adopting dynamic threshold truncation and gamma correction for a shallow band, and performing weighted fusion for a transition region according to probability; s4, carrying out structure strengthening, wherein the structure strengthening comprises centromere local sharpening, stripe phase alignment, edge sensing super-resolution and overlapping region separation; and S5, performing quality evaluation based on the deep band integrity, the band stripe contrast uniformity, the SSIM and the noise density, and triggering adaptive re-enhancement if necessary. According to the scheme, the contrast ratio and details are remarkably improved while the stripe structure and the position relation are kept, and the method has the advantages of light weight, interpretability and cross-sample robustness and is suitable for being integrated into an automatic karyotype analysis process.

The invention discloses an oil leakage detection and traceability method and system for an oil-immersed power transformer, and belongs to the technical field of transformer detection. Irradiating the surface of the transformer by adopting an ultraviolet light source with controllable power to obtain an oil leakage characteristic image of the surface of the transformer; carrying out oil leakage feature image preprocessing through graying, 3 * 3 convolution kernel Gaussian filtering, 3 * 3 weighted median filtering and a contrast-limited adaptive histogram equalization algorithm in sequence; an improved double-threshold maximum between-class variance algorithm is adopted to determine a background and a fluorescence region, and oil stains are accurately extracted through connected region analysis and texture feature screening; a diffusion main direction is determined through a gradient vector weighted voting algorithm, candidate sources are screened from an easy leakage part database in combination with a K nearest neighbor algorithm, and a final leakage source is determined through gray profile analysis and verification. Through multi-algorithm cooperation, the oil leakage detection precision and traceability accuracy are effectively improved, the method is suitable for daily operation and maintenance of the transformer, and the equipment fault risk is reduced.

The invention provides an image enhancement method and system for intelligent image processing, and the method comprises the steps: obtaining original image data in a low-light environment, and the original image data comprises gray level distribution data; performing adaptive histogram equalization processing on the gray level distribution data to generate equalized image data; calculating a cumulative distribution function based on the occurrence frequency of each gray level in the equalized image data; enhancing the optical contrast of the edge region in the equalized image data based on the calculation result of the cumulative distribution function to form enhanced image data; and performing noise reduction processing on the enhanced image data by using a joint noise reduction algorithm, and performing motion compensation on the enhanced image data after noise reduction processing to generate target image data. According to the invention, the dynamic range expansion capability and the noise suppression effect of the image in the low-light environment are improved.

The invention belongs to the technical field of visual SLAM, and discloses a robot real-time positioning method based on ground texture. Acquiring a ground image through a monocular camera at the bottom of the robot, and performing contrast-limited adaptive histogram equalization processing; detecting key points for the first frame, initializing the system, otherwise, tracking the optical flow; matching the current frame feature point tracked by the optical flow with the previous frame feature point, estimating the relative pose transformation of the image, calculating the pose transformation error, and adding the pose transformation error into the factor graph as an odometer constraint; dynamically setting a key frame, and carrying out loopback detection on the key frame; estimating relative pose transformation and errors of the current key frame and the loopback frame, and adding the relative pose transformation and errors into the factor graph as loopback constraints; and performing incremental optimization through the factor graph to obtain an optimized pose. The loopback detection strategy for the ground texture image is provided, loopback detection correctness and scale control are effectively improved, accumulated drift is reduced, and system precision is improved.

The invention relates to the technical field of remote sensing image processing, in particular to a homomorphic filtering-CLAHE remote sensing image enhancement method based on integration strategy multi-target particle swarm optimization, which comprises the following steps: acquiring remote sensing image data, and preprocessing the remote sensing image data; converting the preprocessed remote sensing image into an HSV color space, and extracting a V component in the HSV color space; constructing a frequency domain-spatial domain hybrid enhancement framework of homomorphic filtering and contrast-limited adaptive histogram equalization, and performing enhancement processing on a V-channel component by using the framework; constructing an integrated strategy multi-target particle swarm optimization algorithm; constructing a four-target fitness function including structural similarity, average gradient, information entropy and gray variance, and guiding particles to search in the direction of optimizing a plurality of key image quality indexes at the same time by using the function so as to realize optimal selection of remote sensing image enhancement parameters; the method can effectively enhance the definition and structural integrity of the terrain texture in the remote sensing image of the complex mountainous area.

The invention discloses a line sequence automatic detection algorithm based on machine vision and an implementation method thereof, and belongs to the technical field of machine vision and image processing. Aiming at the problems of low efficiency and poor accuracy of traditional manual detection of a multi-core cable, the invention provides the following technical scheme: preprocessing a collected cable image, and enhancing the image by adopting adaptive histogram equalization of an LAB color space; determining a scanning band at the center of the image and converting the scanning band to an HSV color space; the comprehensive value of the HSV color gradient is calculated, the peak value is detected by adopting an adaptive threshold value, and the boundary of each wire core is accurately positioned; establishing an HSV (hue, saturation and value) feature library containing 10 standard colors, and realizing color matching through weighted distance measurement; and an automatic detection mode and a manual assistance mode are provided. According to the method, the detection accuracy reaches 95% or above, the processing time of a single image is shorter than 1 second, the technical problems of automatic color recognition and sorting detection of the multi-core cable are effectively solved, and the method can be widely applied to cable production quality control.

The invention discloses an infrared image enhancement method and system combining background equalization and detail stretching. The method comprises the steps that an original gray infrared image is decomposed into an image background layer and an image detail layer through an image decomposition method based on a comprehensive average gradient and guided filtering; performing image enhancement processing on the image background layer based on contrast-limited adaptive histogram equalization to obtain an enhanced image background layer; denoising and stretching the detail layer of the image based on adaptive bilateral filtering to obtain a denoised and stretched detail layer; fusing the enhanced image background layer and the denoised and stretched detail layer, and carrying out stretching processing to obtain an enhanced gray infrared image; and performing pseudo-color processing on the enhanced gray infrared image to generate a color infrared image. The method is mainly applied to the technical field of infrared image enhancement, and has wide application prospects in the fields of electrical state monitoring, industrial defect detection and the like. The method has the outstanding advantages of good effect, high practicability, low cost and the like.

The invention discloses an underwater image enhancement method for a complex environment, and the method specifically comprises the following steps: firstly, correcting color cast caused by optical characteristics through an improved white balance algorithm, and effectively recovering colors; secondly, brightness distribution is adjusted and dark part details are enhanced by adopting logarithmic domain gamma transformation, so that the brightness distribution of the image is more uniform, and meanwhile, a self-adaptive histogram for limiting the contrast ratio is equalized, so that the contrast ratio and detail definition of the image are improved; and finally, noise is removed by using bilateral filtering, edge information is reserved, and deblurring processing is performed in combination with a U-Net network. According to the method, the color cast problem caused by optical characteristics is improved by using the improved white balance algorithm, a correct color basis is provided for subsequent processing, pixel distribution is adjusted by using logarithm domain gamma transformation, the importance of details of a bright part is reduced, a dark part region is further enhanced, and the overall brightness distribution is more uniform.

The invention, which relates to the field of power transmission detection, discloses a visualization-based power transmission detection system comprising an image acquisition module, a preprocessing module, a feature extraction module, a defect identification module and an alarm module. A high-definition camera is used for collecting images, a path is planned according to an inspection task during preparation, equipment is debugged, shake is prevented in the process, and a continuous shooting mode is selected; noise reduction is carried out on the image, the image is enhanced after being processed through a wavelet transform algorithm, and the image is standardized into a specific resolution ratio and a specific pixel value through self-adaptive histogram equalization operation; extracting multi-scale features based on a deep learning model, extracting frequency domain features during defect identification, fusing the frequency domain features into vectors, inputting the vectors into a classifier, and optimizing through a feature pyramid network; and finally, sound-light alarm is generated when defects are detected, information is uploaded to a remote monitoring center, geographic information system linkage and local storage are supported, data distribution and decision support are ensured to be timely and accurate, and power transmission equipment is effectively maintained.