66results about How to "Improve defogging effect" patented technology

The invention discloses a real scene image defogging method based on dual-branch CNN-Transform and depth information fusion, and relates to the field of artificial intelligence image processing and machine vision. The method comprises the following steps: generating a depth map: inputting an RGB image into a monocular depth estimator to generate an estimated depth map, wherein the RGB image is a fog-containing image; designing an image defogging model architecture, wherein an image defogging model comprises a feature extraction decoder, a multi-mode feature fusion module and a feature recovery encoder and is used for extracting and synthesizing global features and local features from an RGB mode and a depth mode to carry out image defogging; designing a loss function to carry out model optimization; collecting a data set for model training; and verifying the effectiveness of the trained model by adopting an evaluation index. According to the invention, after the model is designed and trained through a deep learning method, haze removal of the fog-containing image can be quickly and effectively realized.

The invention belongs to the technical field of flue gas treatment, particularly relates to a desulfurized flue gas rectifying and demisting device, and provides the following scheme aiming at the technical problem that the cleaning effect of spraying equipment on an existing demisting device on scaling of demisting blades is limited: the desulfurized flue gas rectifying and demisting device comprises a treatment tower, and the bottom of the treatment tower is fixedly connected with a base; a smoke exhaust pipe is arranged on the upper side of the treatment tower, a smoke conveying pipe is arranged outside the treatment tower, two symmetrical square frame supports are fixedly connected to the inner wall of the treatment tower, the same fixed-point high-pressure flushing module is arranged above the two square frame supports, and the fixed-point high-pressure flushing module comprises two symmetrical first fixing plates. According to the desulfurization flue gas rectifying and demisting device disclosed by the invention, the cleaning effect of the device on scales on the diamond-shaped hole meshes can be greatly improved, the situation that the demisting effect of the device is influenced due to large-area diffusion of the scales caused by the fact that the scales on the diamond-shaped hole meshes cannot be cleaned is avoided, and the demisting effect and the use function of the device are improved.

The invention provides an expressway image defogging method and device in a low-light environment, and relates to the technical field of image processing. The method comprises the following steps: acquiring a first picture data subset in a foggy environment and a second picture data subset in a fogless environment; taking the first picture data subset and the second picture data subset as a real-time picture production data set, and training a two-color feature fusion defogging network model according to the real-time picture production data set; obtaining RGB color space features and HSV color space features in the input image; through a two-color feature fusion defogging network model, according to the RGB color space features and the HSV color space features, obtaining a fusion feature map corresponding to the input image; and carrying out defogging reconstruction on the input image according to the fused feature map. According to the method and the device, the problem that the processed defogged image still has color imbalance due to the fact that the existing defogging method cannot effectively correct color cast in the face of specific challenges under a low-light condition is solved.

The invention discloses an image defogging method for an extremely complex environment, a medium and a program product, and the method comprises the steps: inputting an obtained real-time degraded image into a pre-trained clear image extraction network, and generating a defogged clear image; the training method of the clear image extraction network comprises the following steps: inputting a historical degraded image into an atmospheric light extraction module to obtain an atmospheric light value; inputting the historical degraded image into a clear image extraction network to obtain a clear image; inputting the historical degraded image into a transmission image extraction network to obtain a transmission image; inputting the obtained clear image, the projection image and the atmospheric light value into an atmospheric scattering model to obtain a synthetic degraded image; and based on a first error between the synthesized degraded image and the historical degraded image and a second error between the clear image and the standard clear image, adjusting parameters of the clear image extraction network and the transmission image extraction network, and completing training of the clear image extraction network and the transmission image extraction network. According to the invention, the image defogging effect can be effectively improved.

The invention relates to an antenna device and equipment. The antenna device comprises a dielectric body, a first conductive pattern layer and a second conductive pattern layer. The first conductive pattern layer comprises a first antenna structure, and the first antenna structure is provided with a first radiation branch knot. The second conductive pattern layer and the first conductive pattern layer are located on two different layers of the dielectric body in the thickness direction of the dielectric body respectively, the second conductive pattern layer comprises a first suspension line, and the first suspension line and the first radiation branch knot are at least partially overlapped in the thickness direction to achieve mutual coupling. According to the antenna device, the first suspension line of the second conductive pattern layer and the first radiation branch knot are at least partially overlapped in the thickness direction and are mutually coupled, so that the gain performance of the first working frequency band corresponding to the first radiation branch knot can be improved; in addition, the structure is attractive, the overall layout is compact, a large area is provided for arrangement of the demisting heating unit on the medium body, and the demisting performance can be improved due to the fact that the area of the demisting heating unit is increased.

The invention relates to the technical field of remote sensing image processing, in particular to an ecological environment detection method and system based on a multispectral remote sensing fusion technology, and the method comprises the steps: collecting a multispectral remote sensing image of a target region; determining the foreground information rate of each pixel level in the remote sensing image of each wave band; calculating a distortion coefficient of the remote sensing image of each wave band; obtaining a relative loss coefficient of each pixel level in the remote sensing image of each wave band; dividing the remote sensing image of each wave band into superpixel blocks, and determining the wave band independence of each superpixel block in the remote sensing image of each wave band; screening candidate pixel points in each superpixel block, determining an atmospheric light value, and defogging the remote sensing image of each wave band in combination with a dark channel prior method; and performing ecological environment detection based on the remote sensing images of all wavebands after defogging. Therefore, the accuracy of ecological environment detection is improved.

The invention discloses a weak supervision defogging method based on uncertainty driving, and the method employs a weak supervision defogging network of a U-Net architecture, and the weak supervision defogging network comprises a regional dynamic attention module RDAM, a lower sampling layer, a multi-scale feature fusion module MSFFM, and an upper sampling layer. The RDAM is used for processing an input image or the output of a previous layer of RDAM, constructing a dynamic convolutional layer and obtaining a regional dynamic attention feature map through the dynamic convolutional layer; the down-sampling layer is used for down-sampling the output of the RDAM and reducing the spatial resolution; the MSFFM is used for processing input features through a window multi-head self-attention mechanism so as to capture a long-distance dependency relationship between pixels and performing multi-scale feature extraction and fusion; the upper sampling layer is used for recovering the feature map output by the MSFFM to the resolution ratio of the feature map output by the MSFFM of the upper layer and performing feature splicing with the feature map output by the MSFFM of the upper layer; and enabling the up-sampled feature map to sequentially pass through a 3 * 3 convolutional layer, a ReLU activation function and a 1 * 1 convolutional layer, and finally outputting a defogged image.

The invention belongs to the technical field of illumination, and particularly relates to a self-balancing waterproof structure of an ultra-thin LED outdoor projection lamp, which comprises a shell, a sealing plate is fixedly connected to the surface of the shell, a glass cover is connected to the sealing plate in a sealing manner, a rear cover is fixedly connected to one side, far away from the shell, of the sealing plate, and a demisting mechanism is arranged between the rear cover and the shell; water vapor condensed on the inner wall of the glass is evaporated through high-temperature gas or heated air in the heat dissipation bin, water mist is prevented from being formed, the lighting effect of the lamp is guaranteed, flexible utilization of the high-temperature gas in the heat dissipation bin is achieved, the self-balancing waterproof structure and the demisting mechanism are arranged in a matched mode, and the self-balancing waterproof structure and the demisting mechanism are arranged in a matched mode. And the service life and reliability of the lamp are further prolonged.

The present invention relates to the field of image defogging, and specifically discloses a 4K image defogging method, device and storage medium based on weakly supervised contrast learning, including S1, obtaining a dark channel prior image #imgabs1# after dark channel prior processing of a foggy image #imgabs0#; weighted fusion of the foggy image #imgabs2# and the processed image #imgabs3#, and finally obtaining a fused image #imgabs4#; S2, inputting the fused image #imgabs5# into a U-net convolutional neural network structure for processing, and inserting a residual block and introducing an SC attention mechanism during processing; S3, using PatchGAN as a discriminator, which divides the restored image #imgabs6# into many small overlapping areas, judging whether each small area is part of the real image, using the least squares GAN loss to train the network, and finally discriminating and obtaining the restored fog-free image #imgabs7#; S4, contrast learning loss function. The beneficial effects achieved by the present invention are: the high-resolution image defogging algorithm is short in time and can maintain a high-precision defogging effect.

The invention discloses a deep learning-based artillery barrel target image defogging method and system, and the method comprises the steps: inputting an original artillery barrel target image into a trained defogging enhancement network model for processing, and enabling the defogging enhancement network model to comprise an improved U-Net module, an AOD-Net module and an atmospheric scattering model layer which are connected in sequence; multi-scale features of an original artillery barrel target image are extracted through an improved U-Net module, a multi-scale feature map is output to an AOD-Net module, the AOD-Net module extracts features of the multi-scale feature map in different receptive fields through different convolutional layers, multi-scale feature fusion is achieved, an input value of an atmospheric scattering model layer is output, and the target image is obtained through the AOD-Net module. And the atmospheric scattering model layer obtains a defogged artillery barrel target image based on the input value. According to the method, U-Net and AOD-Net are connected in series to construct a model, a new model optimizer is provided, a gradient descent algorithm is optimized, and meanwhile, an adaptive weight loss function is introduced, so that efficient defogging processing under a complex haze condition is realized.

According to the antenna device and the vehicle, a first feed part is electrically connected with a non-closed first conductive ring, antenna signals of a first working frequency band can be radiated and received, and due to the fact that the first feed part is arranged to be annular, the occupied area can be reduced; besides, research finds that when the second transverse strip and the third transverse strip are arranged at intervals and are mutually coupled, the average gain of the antenna signal of the first working frequency band can be increased, the effect of improving the antenna performance of the antenna signal of the first working frequency band is achieved, and the stability is better. Besides, the gain does not need to be correspondingly increased by increasing the area of the first antenna structure, that is, the area size of the first antenna structure can be correspondingly reduced, so that the area of the demisting heating unit can be increased to improve the demisting effect.

The invention relates to a defogging network based on dynamic routing and three-dimensional fusion. The network comprises a dual-branch network Twood-branch network, a dynamic routing feature module DRFM and a clear image reconstruction module CIRM, the input end of the double-branch network Twood-branch network is used for inputting a foggy image I, the output of the double-branch network Twood-branch network is connected with the input of the dynamic routing feature module DRFM, the output of the dynamic routing feature module DRFM is connected with the input of the clear image reconstruction module CIRM, and the output of the clear image reconstruction module CIRM is used for outputting a defogged image.

The invention relates to the technical field of image processing, in particular to an image defogging method based on dark channel prior, which comprises the following steps of: receiving an image to be processed, traversing each pixel in the image to be processed, identifying the gray value of each pixel, and determining the maximum gray value of the pixel in the image to be processed; on the basis of a dark channel prior defogging algorithm and in combination with the maximum gray value of the pixels in the to-be-processed image, the pixel values of the restored pixels in the to-be-processed image are calculated, the dark channel prior image defogging algorithm serves as the basis, the image defogging effect is improved to a large extent through a correction mode, and the image defogging efficiency is improved. Further based on the comparison of the original image and the defogged image, the defogging optimization is further performed on the area with the poor defogging effect in the defogged image, so that the overall effective defogging processing of the original image is realized, the quality of the defogged image and the actual defogging effect are improved, and the defogging efficiency is improved. The method is especially suitable for defogging processing of foggy images with the blank areas occupying a large proportion in the whole image.

The invention relates to the technical field of desulfurization and demisting, and discloses a demister with a water circulation function, comprising a mounting rack and a cleaning box fixedly connected to the mounting rack; the first electric telescopic rod is fixedly connected to the mounting frame, and a mounting plate is fixedly connected to the bottom end of the first electric telescopic rod; according to the demister with the water circulation function, water is supplied to the cleaning box through the water circulation mechanism, then a baffle plate is driven to be led into the cleaning box, then a mounting pipe and the baffle plate are driven to rotate in the cleaning box, and meanwhile cleaning water in the cleaning box is disturbed through fixing of an air bag; meanwhile, mist particles on the baffle plates fall off under the action of centrifugal force, and the baffle plates are washed and cleaned through the cleaning nozzles on the cleaning pipes, so that the mist particles on the baffle plates are fully cleaned, the automatic cleaning effect on the demister is greatly improved, and the subsequent smoke demisting effect of the demister is further improved.

The present application discloses an image enhancement method, device, equipment and storage medium under severe weather conditions, and relates to the field of image enhancement. The method reads an image frame and performs LAB color space conversion to determine the degraded image frame and the corresponding image type therein; if it is a dust image, it is converted into a grayscale image and a power operation is performed on the grayscale value to obtain a dust-free image; if it is a haze image, image edge filtering is performed and a transmittance image is extracted; when the filtered image contains a sky area, the transmittance value of the area is extracted and deviation correction is performed to obtain a corrected transmittance image; based on the corrected transmittance image, image defogging is performed to obtain a haze image. The scheme distinguishes the degraded image frames and the corresponding image types caused by severe weather according to the color cast factor and chromaticity, and adopts different image enhancement schemes for different image types, especially for haze images, gradient domain guided filtering and transmittance optimization of the sky area are performed to obtain a clearer defogging image.

The invention discloses a titanium dioxide calcination tail gas dust content ultralow emission method, relates to the technical field of titanium dioxide production tail gas treatment, and solves the technical problem of high tail gas dust content caused by the fact that actual tail gas treatment cannot meet the emission standard in the existing titanium dioxide calcination process. The method comprises the following steps: S1, carrying out primary spraying treatment on tail gas obtained after titanium dioxide is calcined so as to preliminarily remove dust and sulfur dioxide pollutants in the tail gas; s2, the tail gas treated in the step S1 is subjected to secondary water spraying treatment so as to absorb sodium sulfite generated in the step S1; and S3, the tail gas treated in the S2 is subjected to demisting treatment, so that acid mist in the tail gas is removed, and the tail gas reaches the emission standard. The device has the advantages that pollutants generated in the tail gas calcination process can be fully absorbed, and the tail gas dust content is reduced.

The utility model discloses a three-phase separator for natural gas treatment, which comprises a separator body, a mounting pipe is arranged at the top of the separator body, a plurality of defoggers are arranged in the mounting pipe from top to bottom, adjacent defoggers are connected together, an exhaust hood is arranged at the top of the mounting pipe, and the exhaust hood is connected with the defoggers. The exhaust hood and the demister are detachably connected with the mounting pipe, the bottom end of the exhaust hood is tightly pressed on the demister, and a filter element is arranged on the inner side of the exhaust hood and used for finally filtering the natural gas before the natural gas is exhausted. A plurality of demisters are mounted at the top of the separator body and are overlapped, so that a better demisting effect is conveniently achieved, an exhaust hood is arranged at the top of each demister, the exhaust hoods can be quickly disassembled and assembled, the demisters can be directly taken out after the exhaust hoods are disassembled, workers can conveniently replace or maintain the demisters, and the working efficiency is improved. And the repair and maintenance of the three-phase separator are greatly facilitated.

A demisting cleaning mechanism comprises an exhaust pipe, the exhaust pipe is fixedly communicated with one side of a desulfurization box, an exhaust fan is fixedly mounted in the exhaust pipe, the end part of the exhaust pipe is communicated with a water-cooling demisting box, a demister body is fixedly mounted in the water-cooling demisting box, and the demister body is fixedly mounted in the demister body. The demister comprises a demister body, a rotary cold water pipe is rotationally connected into the demister body, an annular water pipe sleeves the rotary cold water pipe, a fixed box is rotationally connected to one end of the rotary cold water pipe, a first bevel gear is fixedly mounted at one end of the rotary cold water pipe, and a water inlet pipe is fixedly connected to one side of the fixed box; a hollow rotating shaft is fixedly installed between the water inlet pipe and the rotating cold water pipe, a second bevel gear is meshed with the bottom of the first bevel gear, a rotating shaft is fixedly connected with the bottom of the second bevel gear, a fixing shell is fixedly connected with the bottom of the water-cooling demisting box, and the cleaning effect of the device is good.

The present invention relates to the field of image processing technology, and in particular to an image defogging method, system, electronic device and storage medium. The image defogging method comprises: constructing an image defogging model and training it; wherein the image defogging model comprises: a shallow feature extraction module, a feature fusion module and an image reconstruction module; the shallow feature extraction module is used to extract shallow primary features of a sample foggy image; the feature fusion module is used to encode, decode and fuse shallow primary features to obtain fused image features; the image reconstruction module is used to perform image channel conversion on the fused image features to obtain converted image features, and fuse the converted image features with the sample foggy image to obtain a sample defogging image; the foggy image to be processed is input into a trained image defogging model to obtain a target defogging image. The present invention improves the efficiency and accuracy of defogging, effectively eliminates the influence of fog on the image, and restores clearer and more accurate image details.

The invention discloses an image defogging method and system based on multi-level fusion and parallel filtering, and belongs to the field of image processing.The method comprises the steps that firstly, a foggy image is input, and useful low-level and high-level features are extracted through the multi-level feature fusion technology; then, designing a parallel filter to process the blurred image for feature extraction, and ensuring that texture details are recovered while capturing a large-area blurred region; besides, an enhanced distributed attention unit is constructed, uneven haze distribution is effectively processed, and extracted features are adaptively fused through learned spatial weight features. And finally, inputting the fused features into a defogging model to generate a clear output image. The invention further provides a system for realizing the method, evaluation shows that the method obviously improves the defogging effect under the condition that only 6.92 M parameters are used, and the method has a good application prospect.

The application discloses a deep learning-based artillery barrel target image defogging method and system, which comprises the following steps: inputting an original artillery barrel target image into a trained defogging enhancement network model for processing, wherein the defogging enhancement network model comprises an improved U-Net module, an AOD-Net module and an atmospheric scattering model layer connected in sequence; the improved U-Net module is used to extract multi-scale features of the original artillery barrel target image, and output the multi-scale feature maps to the AOD-Net module; the AOD-Net module is used to extract features under different receptive fields of the multi-scale feature maps through different convolution layers, realize multi-scale feature fusion, and output input values of the atmospheric scattering model layer; and the atmospheric scattering model layer is used to obtain the defogged artillery barrel target image based on the input values. The model is constructed by connecting the U-Net and the AOD-Net in series, a new model optimizer is proposed, the algorithm of gradient descent is optimized, and a self-adaptive weight loss function is introduced, so that efficient defogging processing under complex fog and haze conditions is realized.

The utility model discloses an anti-interference integrated liquid crystal display module with a heating function, which comprises a backlight plate, the upper part of the backlight plate is provided with a TFT (Thin Film Transistor) glass main body, ITO (Indium Tin Oxide) heating glass and ITO shielding glass, the TFT glass main body, the ITO heating glass and the ITO shielding glass form a functional glass unit, and the upper part of the functional glass unit is provided with a cover plate. As the ITO heating glass is positioned above the TFT glass main body, the cover plate can be rapidly heated, and the demisting effect is good. And uniform heating can be realized by using the ITO to heat the plating layer. The shielding pattern of the ITO shielding plating layer is utilized, interference signals can be effectively shielded, and the effect is excellent. The functional glass unit is composed of the TFT glass body, the ITO heating glass and the ITO shielding glass, the application range of the liquid crystal module is greatly expanded, the liquid crystal module has the functions of shielding, demisting, displaying and the like at the same time, the liquid crystal module can be used in different complex environments, and the competitiveness of products is improved.

The present invention belongs to the field of flue gas treatment technology, and in particular to a flue gas rectifying and demisting device for desulfurization. Aiming at the technical problem that the spray equipment on the existing demisting device has limited cleaning effect on the scale of the demisting blades, the following scheme is proposed, including a treatment tower, the bottom of the treatment tower is fixedly connected with a base, and the upper side of the treatment tower is provided with a smoke exhaust pipe, and the outside of the treatment tower is provided with a smoke delivery pipe, and the inner wall of the treatment tower is fixedly connected with two symmetrical square frame brackets, and the same fixed-point high-pressure flushing module is provided above the two square frame brackets, and the fixed-point high-pressure flushing module includes two symmetrical fixed plates. A flue gas rectifying and demisting device for desulfurization disclosed in the present invention can greatly improve the cleaning effect of the device on the scale on the diamond hole mesh, avoid the situation that the scale on the diamond hole mesh cannot be cleaned and the scale spreads over a large area, which affects the demisting effect of the device, and improves the demisting effect and use function of the device.

The present invention relates to a method and system for defogging information of foggy images. The method comprises: obtaining a foggy image for image preprocessing; performing a first image enhancement on the processed image data based on the ACE algorithm to obtain intermediate image data; performing grayscale gamma transformation on the intermediate image data, adjusting the gamma value of the intermediate image data, and performing contrast adjustment and a second image enhancement to obtain image data to be filled; using a coloring model to fill missing information on the image data to be filled, and outputting a defogged image. By performing two image enhancements on the collected foggy image, and the second image enhancement is combined with a coloring model to fill missing information, the missing information of the original foggy image is largely restored, which is convenient for computer analysis and subjective observation of the human eye, and can effectively solve the defogging operation of the foggy image, so that the image information is more realistically displayed, and the defogging effect of the image is improved.

The invention relates to the technical field of image processing, in particular to a single image rain and fog removing method based on transmission characteristic self-adaptive calibration, which comprises the following steps of: uploading a foggy image, analyzing an atmospheric light value and transmissivity in a foggy image acquisition stage, and calculating a foggy image; constructing a foggy image forming model based on the atmospheric light value and the transmissivity in the foggy image acquisition stage in combination with the foggy image; the method comprises the following steps: acquiring a foggy image, performing multi-scale decomposition on the foggy image by applying a Gaussian pyramid to obtain sub-images of different scales, and calculating a characteristic value on each sub-image; according to the method, the brand new corrected transmissivity and the atmospheric light value are configured for the existing foggy image forming model based on the specified logic, so that the transmissivity and the atmospheric light value in the original foggy image forming model are replaced based on the brand new corrected transmissivity and the atmospheric light value; and further converting the foggy image forming model into a fogless image output formula to realize the output of the fogless image so as to improve the defogging effect of the output fogless image.

The invention discloses a conveyor belt defect detection method based on image defogging processing, and solves the problems of poor defogging effect and low defect detection accuracy in the prior art. The method comprises the steps that an original image is input into a defogging branch network and a backbone network, the defogging branch network carries out normalization and correction processing on the original image, and multi-scale defogging image features are obtained; the backbone network performs feature extraction on the original image to obtain multi-scale original image features; performing feature fusion on the multi-scale defogged image features and the multi-scale original image features to obtain fused features; and inputting the fused features into a neck network for processing, and inputting the processed features into a detection head for feature detection.

The invention provides a polarization image defogging method based on a neural network, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a plurality of fogged polarization images, and generating a polarization image package; constructing a neural network defogging model; and based on the constructed neural network defogging model, carrying out defogging processing on the foggy polarization image in the generated polarization image packet to obtain a fogless image. According to the image defogging method, the deep learning and the polarization imaging technology are combined, the problem that the calculation complexity of the polarization imaging technology is high can be effectively solved, the overall generalization ability and defogging level are improved, and better robustness and high adaptability can be shown in the face of different types of fog and different degrees of haze.

The present application provides a method and device for defogging highway images in a low-light environment, and relates to the field of image processing technology. The method includes: obtaining a first image data subset in a foggy environment and a second image data subset in a fog-free environment; using the first image data subset and the second image data subset as a real-time image production data set, and training a two-color feature fusion defogging network model based on the real-time image production data set; obtaining RGB color space features and HSV color space features in the input image; obtaining a fusion feature map corresponding to the input image based on the RGB color space features and the HSV color space features through the two-color feature fusion defogging network model; and defogging and reconstructing the input image based on the fusion feature map. The present application solves the problem that when facing specific challenges under low-light conditions, existing defogging methods often cannot effectively correct color deviation, resulting in the problem that the processed defogging image still has color imbalance.

The application discloses a kind of light multi-scale image defogging methods based on attention mechanism, the method is first to the feature extraction of fog image;Then the feature map obtained is input MDSCA- NET network, and the fog-free image is reconstructed;The MDSCA- NET network includes several image defogging models, model fusion module;The image defogging model includes feature attention module, fog-free map generation module;The feature attention module is used to extract the pixel feature in feature map;The fog-free map generation module is used to generate initial defogging image for pixel feature map;The model fusion module is used to use alternating direction multiplier optimization algorithm to carry out model fusion to several image defogging models, and output final defogging image.Experiments show that the MDSCA- NET network proposed in the application obtains significant results under the comprehensive measurement of model performance and parameter quantity.

The utility model discloses a stem cell culture device convenient to observe, which belongs to the technical field of stem cell culture and comprises a shell, the lower surface of the shell is in bolted connection with a base, the inner wall of the shell is in bolted connection with a placing plate, and the back of the shell is in bolted connection with an electric fan; the air blowing assembly is arranged on the inner wall of the door plate, when the air blowing assembly works, the air blowing assembly is matched with the demisting assembly to work, the demisting effect is optimized, and the air blowing assembly comprises fan blades rotationally arranged on the inner wall of the door plate. When the stem cell culture device convenient to observe works, a door plate is opened, materials are placed on a placing plate, then the door plate is closed, workers can directly observe the conditions through an observation window when needing to observe the conditions, and when the observation window fogs, the workers can start a motor through a controller to enable the motor to drive a long pin to rotate, so that the materials can be conveniently observed. And a long pin is matched with a limiting rod, so that a brush plate moves and cleans an observation window, and a worker can observe the condition in the shell more easily.