110 results about "Retinex algorithm" patented technology

Provided is a low-illumination image adaptive enhancement method, an apparatus thereof, a device and a storage medium. The method includes: inputting a low-illumination image to be enhanced into a pre-trained low-illumination image adaptive enhancement model to obtain an enhanced image. A method of training the low-illumination image adaptive enhancement model includes: acquiring a training set containing the low-illumination image and the corresponding reference image; constructing the low-illumination image adaptive enhancement model based on a Retinex algorithm. The low-illumination image adaptive enhancement model includes a projection module, an illumination component module, a reflectance component module and an enhancement module. The training set is used to train the low-illumination image adaptive enhancement model, and the trained low-illumination image adaptive enhancement model is obtained. The brightness and the contrast of the image are enhanced, and at the same time, the color and structure information of the image is restored effectively.

The invention belongs to the technical field of computer vision, and discloses a wire harness crimping defect detection system and method based on machine vision, and the system comprises a multispectral light source control module which is used for employing an annular LED light source and coaxial light mixed illumination and adapting to different wire harness surface materials by dynamically adjusting the angle and intensity of the light source; the multi-dimensional perception acquisition module is used for acquiring wire harness image data and crimping geometric data in real time based on the dynamically adjusted light source angle and intensity; the visual enhancement fusion module is used for de-noising the wire harness image data by using a non-local mean de-noising algorithm and dynamically adjusting and controlling a similarity measurement range through a smooth factor; superposing a multi-scale Retinex algorithm, and enhancing the texture contrast of the wire harness image data through the local contrast; the wire harness image data and the crimping geometric data are fused, and a crimping part multi-mode characteristic graph is constructed; the accuracy of defect detection is facilitated, and potential problems can be found and solved in time.

The invention relates to the technical field of image recognition data processing, in particular to a pipeline element library creating method based on a model training and image recognition technology, which comprises the following steps of: acquiring pipeline element image data with depth information through a multispectral imaging scheme, executing Retinex algorithm illumination equalization and morphological restoration through a cascade preprocessing pipeline, and establishing a pipeline element library. A standardized data set associated with the metadata is generated. A multi-task joint learning framework is adopted to integrate ResNet-50, HRNet and Mask R-CNN networks, a bottom convolution feature extraction layer is shared, oil stain and strong reflection antagonism data synthesized by a generative adversarial network is injected, model parameters are optimized in combination with a progressive training strategy, and a lightweight MobileNetV3 model is output. Model parameters and an index structure are dynamically updated, and system self-calibration is achieved in combination with a cross-device calibration protocol and data consanguinity tracking. According to the method, through automatic data labeling, multi-task feature multiplexing and retrieval feedback closed loop, the construction efficiency of the pipeline element library is effectively improved, and the recognition error rate in a complex environment is reduced.

The invention relates to the technical field of image processing, in particular to an automatic snow removal operation method and system based on image enhancement processing, and the method comprises the following steps: collecting an original accumulated snow image of a road surface, decomposing an image brightness component into a low-frequency component and a high-frequency component through a multi-scale Retinex algorithm, and generating an enhanced image; on the basis of the enhanced image, calculating a joint gradient of saturation and brightness in a pixel point HSV space, and generating a snow area binary segmentation image by adopting an improved Otsu dual-threshold method; the accumulated snow compactness grade is judged according to the texture uniformity; and driving the execution mechanism to spray the snow removing agent according to the path coordinates, and adjusting the spraying concentration of the snow removing agent in combination with the compactness grade. According to the method, the snow boundary definition and the plaque internal texture identifiability are improved, a high-quality image foundation is laid for subsequent segmentation and analysis, and the adaptive capacity and stability of the system under the natural light condition are enhanced.

The invention discloses a locomotive sanding detection method. The method comprises the following steps: step 1, judging whether a license plate number is identified or not; 2, broadcasting sanding notifications of all the positions according to a preset time difference; step 3, acquiring a video shot by the monitoring camera; 4, dark light enhancement is carried out through a U-Net model, a YOLOV5 model is input, and whether the monitoring camera is shielded or not is judged; step 5, carrying out illumination adaptation processing on a plurality of continuous frames of images of the video obtained in the step 3 through a CLAHE algorithm; step 6, carrying out image enhancement through a Retinex algorithm and a U-Net model; 7, inputting a 3D-CNN model to carry out feature extraction and time sequence modeling; and step 8, inputting a Transform Encoder model, and outputting the probability of each sanding flow rate. According to the method, the detection of the sand spraying amount can be completed, and the detection of whether the monitoring camera is shielded or not can be completed before the detection of the sand spraying amount.

The invention relates to the technical field of image enhancement, in particular to a surface process defect detection method and system for copper foil production, and the method comprises the steps: collecting a copper foil image in a production process; obtaining each image block in the copper foil image; performing edge detection on the copper foil image to obtain each edge line in the copper foil image; determining a texture feature coefficient of each image block, constructing an illumination contrast coefficient of each image block, correcting a standard deviation parameter in a Gaussian filter, and performing image enhancement on each image block by using the Gaussian filter after parameter correction and a local Retinex algorithm; and carrying out surface defect detection on the copper foil through the enhanced copper foil image. Therefore, the precision of copper foil surface process defect detection is improved.

The invention relates to the technical field of image processing, and particularly discloses a video monitoring image processing system which comprises a dynamic video acquisition module, a self-adaptive illumination enhancement and denoising module and an image segmentation module. The dynamic video acquisition module acquires video data through a fixed camera and a movable camera, and supports various resolutions and frame rates. The adaptive illumination enhancement and denoising module utilizes a multi-scale Retinex algorithm to realize brightness equalization, and combines bilateral filtering denoising to effectively improve the image quality under the low illumination condition. The image segmentation module is based on an improved SegNet network, and greatly improves segmentation precision and reasoning speed by introducing residual connection, a channel attention mechanism and a self-attention mechanism and adopting an up-sampling strategy combining bilinear interpolation and deconvolution. Experimental results show that the segmentation precision of the system is improved by 6.2%, and the reasoning speed is improved by 32%. The system is suitable for image processing and target segmentation in low illumination and complex scenes.

The invention belongs to the technical field of image data processing, and discloses a driver fatigue state early warning method and system based on face recognition tracking, and the method comprises the steps: extracting a face region through semantic segmentation, splitting a single-channel gray-scale map, calculating the local complexity and noise degree of a pixel point, obtaining a window adjustment coefficient, and carrying out the adjustment of the window adjustment coefficient; the size of the adaptive window is determined, different pixel region features can be adapted in a targeted manner, and the processing deviation caused by a fixed window is effectively reduced; an illumination component is subsequently extracted based on an adaptive window, and the image is enhanced through a Retinex algorithm, so that the face image quality under complex illumination can be improved, and the detail definition is improved; and finally, the fatigue degree is detected in combination with a PERCLOS algorithm, so that the facial fatigue characteristics of the driver can be more accurately captured, and effective recognition and early warning of the fatigue state are realized. According to the invention, continuous and stable monitoring can be realized in the driving process, reliable guarantee is provided for driving safety of a driver, and the risk of accidents caused by fatigue driving is reduced.

The invention discloses an autoclaved aerated concrete member surface defect intelligent identification system based on image processing, and particularly relates to the field of defect identification, comprising an image acquisition module, an image preprocessing module, a defect candidate region extraction module, a defect identification and classification module, and a result output and alarm module; according to the method, a high-definition industrial camera is used for collecting a component surface image, and adaptive median filtering and a Retinex algorithm are adopted for image denoising and enhancement, so that the influence of noise and uneven illumination is eliminated; utilizing an improved multi-threshold segmentation and Canny edge detection algorithm to accurately extract a defect candidate region; the method comprises the following steps: extracting three types of feature parameters of shape, texture and gray scale, and inputting the three types of feature parameters into a deep learning model taking ResNet50 as a basic network to realize automatic identification and classification of four types of typical defects of cracks, holes, unfilled corners and surface peeling; and finally, the system divides severity levels according to the defect size, and triggers differentiated visual alarm and linkage control.

The invention relates to the technical field of image processing, in particular to an image data processing method and system for digital management of environmental protection equipment, and the method comprises the steps: collecting a real-time image of an aeration area through a waterproof camera disposed above an aeration tank, detecting the definition of the camera regularly, and carrying out the cleaning when the definition is lower than a preset value; a self-adaptive Retinex algorithm is adopted for the real-time image of the aeration area to eliminate water surface reflection interference, noise caused by water turbidity is reduced through a multi-frame weighted average method, and a processed image is obtained; segmenting a bubble region in the image based on a U-Net network and calculating a bubble distribution parameter; comparing the bubble distribution parameter with a bubble distribution threshold value, and generating an aeration rate optimization instruction according to average dissolved oxygen data obtained by a dissolved oxygen sensor; and controlling the air blower based on the aeration rate optimization instruction. The power of the air blower can be better adjusted according to the bubble condition of the aeration tank, so that more energy is saved.

The invention relates to the field of image processing, in particular to a high-frequency welded pipe defect detection method and system based on machine vision, a welded pipe image is shot through a camera, a detection area is extracted by utilizing a YOLOv5 algorithm, an improved multi-scale Retinex algorithm is adopted, an adaptive fusion weight is calculated according to image characteristics, illumination equalization is carried out, and highlight and shadow are effectively processed. Then, a defect area is positioned through Canny edge detection and connected domain analysis, and GLCM texture features and HU moment geometric features of the defect area are extracted; the features and defect labels are used for training a CNN classification model, finally, the trained CNN model is used for analyzing a high-frequency welded pipe detection area image obtained in real time, and whether surface defects exist or not and the degree of the surface defects are judged. According to the method, improved MSR, target detection, feature extraction and deep learning are fused, and the detection efficiency and accuracy are remarkably improved.

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 a food waste detection method and system based on image processing, belongs to the field of image recognition, and aims to realize efficient and automatic recognition and quantification of kitchen waste. According to the method, residual food images are collected at multiple periods and multiple angles in a kitchen garbage can or a dinner plate recovery area through high-resolution and multi-spectral imaging equipment, preprocessing is carried out in combination with an improved Retinex algorithm and a space self-adaptive denoising technology, and the image quality is improved. Afterwards, fine segmentation of a food area is achieved through a multi-scale super-pixel segmentation and graph segmentation algorithm, and multi-category intelligent recognition is conducted on remaining food through a recognition network fused with multi-modal features. The system further combines stereoscopic vision and Monte Carlo sampling to dynamically and accurately count the volume or weight of various residual foods. The method has the advantages of high adaptability and accurate statistical result, and can provide data support for catering management, resource recovery, nutrition evaluation and the like.

According to the water surface floating object identification method in the low-light environment provided by the invention, through an improved Yov7 target detection model, a Gold-YoLo feature fusion module, a CBAM-Enhanced Retinx-Net image enhancement network and an MPDIOU loss function, the detection precision of floating objects in the low-light environment is remarkably improved. The CBAM attention mechanism and the adaptive enhancement strategy effectively enhance the brightness, contrast and detail information of the image, and solve the problem of excessive enhancement of the traditional Retinex algorithm under low illumination. The Gold-YoLo module improves the feature extraction capability of the floating object through multi-scale feature interaction. The MPDIOU loss function optimizes the bounding box regression precision and improves the stability in a complex environment. Experimental results show that the method can effectively reduce the omission ratio and improve the small floater identification precision, and is suitable for real-time floater detection in the fields of water quality monitoring, ocean protection and the like.

The invention discloses an optical fiber connector defect detection method and system based on deep learning, and belongs to the field of optical communication equipment quality detection. Aiming at the problems of insufficient feature extraction, insufficient utilization of multi-modal data, poor environmental adaptability and the like of a traditional detection method, detection is realized through a multi-modal data fusion-feature enhancement-dynamic calibration closed-loop architecture. An improved Retinex algorithm and the like are adopted for preprocessing data, features are fused through a cross-modal attention mechanism and the like, features are enhanced through YOLOv8, GAN and the like, and a threshold value is dynamically calibrated in combination with Bayesian optimization and the like. Various defects can be recognized, the detection accuracy rate is larger than or equal to 99.5%, the false alarm rate is smaller than or equal to 0.1%, full-process intelligentization of detection is achieved, and the method is suitable for high-precision defect recognition.

The invention belongs to the technical field of digital image processing, and relates to an unpaired low-illumination image enhancement method based on Fourier transform. The invention provides an innovative scheme, an unpaired training strategy is adopted, and dependence on paired data is avoided. The method comprises the following steps: firstly, acquiring a low-illumination image and an unpaired high-illumination image, then extracting illumination and reflectivity components from the low-illumination or high-illumination image by using a pre-training separation network based on a Retinex algorithm, and training an enhanced reflectivity component; then, the illumination components are sent to a cyclic consistency generative adversarial network (CycleGAN) for non-pairing training; and finally, multiplying the enhanced reflectivity by the illumination component to obtain a final enhanced image. Through combination of the pre-trained reflectivity graph and the non-pairing training, the illumination and detail recovery capability of the image is effectively improved, the adaptability and generalization capability of the model are high, a remarkable image enhancement effect can be realized without data pairing, and the method has wide practical application potential.

The invention provides a cultural relic image acquisition system combining a depth-of-field stacking technology and a high dynamic range (HDR) splicing technology, and belongs to the technical field of image acquisition. According to the system, depth-of-field stacking and HDR splicing technologies are combined, focal plane positions and various exposure times are set firstly, and different exposure images are collected by each focal plane. And carrying out space alignment and brightness correction on the image by adopting an image registration algorithm based on feature points, and carrying out HDR synthesis on different exposure images of each focal plane to obtain a high dynamic range image. And calculating a definition evaluation value through a Laplace operator, and selecting the pixel with the highest definition to generate a panoramic depth image. Image feature point matching and alignment are carried out on multiple panoramic depth synthesis, seamless fusion is carried out through a smooth transition function based on weight, and the problems of splicing gaps and inconsistent brightness are eliminated. And finally, enhancing details by using a multi-scale Retinex algorithm, and performing color correction by using a standard color card to generate a high-precision and large-format cultural relic panoramic depth image.

The invention discloses a welding defect identification method and device based on artificial intelligence, and relates to the technical field of welding defect identification. Comprising the following steps that S1, continuous frame images are collected to obtain welding appearance images, and accurate time is marked on each frame image; s2, carrying out HDR fusion reconstruction; s3, Fourier transform is carried out on the arc light periodic noise, and an improved Retinex algorithm is combined to eliminate non-uniform illumination influence; s4, deep learning denoising based on physical simulation; s5, multi-modal data fusion and welding defect decision making are carried out; s6, performing real-time processing and incremental learning; according to the invention, application of artificial intelligence in welding defect identification is realized, the detection accuracy and efficiency are improved, the welding defect cost is reduced, and the intelligent development of welding quality detection is promoted.

The invention relates to the technical field of image processing, and discloses an underwater image enhancement method based on a Retinex algorithm, and the method comprises the steps: firstly obtaining an underwater original image, and extracting multi-dimensional feature information through a preprocessing module (including a histogram analysis unit, a filtering analysis unit and the like); and multi-scale processing is supported, a target processing scale is detected during multi-scale simultaneous processing, and the feature confidence is calculated to screen the optimal feature. The Retinex algorithm processing unit adopts a multi-branch fusion structure (color correction, contrast adjustment and detail recovery branch), generates coefficients and fuses the coefficients to realize enhancement, and can dynamically adjust weights and parameters according to coefficient differences. The method comprehensively extracts features, performs adaptive multi-scale processing, balances the enhancement effect, improves the underwater image quality, and is suitable for underwater image optimization.

The invention discloses an intelligent automobile part quality detection method based on machine vision, and relates to the related field of machine vision and quality detection, and the method comprises the steps: S1, collecting an automobile part surface image through an image collection system; s2, a multi-scale Retinex algorithm based on guided filtering is adopted to pre-process the surface image of the automobile part, and the image quality is improved; s3, using a YOLOv11 instance segmentation model to carry out intelligent detection on the preprocessed automobile part surface image, and identifying and positioning a defect area of the image; and S4, obtaining a defect detection result of the surface of the automobile part, and generating a quality detection report. The problem that in the prior art, the quality detection precision of automobile parts is low is solved. According to the invention, the improved image enhancement algorithm is adopted to improve the image quality, and the learning ability of the neural network is utilized to improve the stability and robustness of quality detection. According to the intelligent quality detection method, the maintenance work of automobile part production is more targeted, and the maintenance and repair cost is effectively reduced.

The invention discloses an electronic detonator bridge wire welding quality on-line detection method and system, and the method comprises the steps: dynamically collecting a visible light and near-infrared dual-channel image, matching a track speed through an encoder, and compensating a pixel; registering, fusing and preprocessing the image, and extracting contour, texture and geometric morphology features; the quality is judged by using an improved ResNet-50 model, and unqualified products are positioned and marked; detection data are classified and stored to realize tracing; the system comprises an image acquisition unit, a preprocessing unit, a feature extraction unit, a quality judgment unit, a positioning marking unit and a classified storage unit, and the image quality is improved through multispectral imaging and dynamic frame rate matching in combination with phase correlation registration and an improved Retinex algorithm; feature extraction is enhanced by means of an attention mechanism, and accurate classification and positioning of defects are realized; a multi-level data mapping structure is established, efficient tracing is supported, the detection efficiency and accuracy are greatly improved, and the high-speed detection requirement of an automatic production line is met.

The invention relates to a citrus surface defect detection method based on a Retinex algorithm and deep learning, and the method comprises the following steps: collecting citrus images of different defect types under different illumination conditions, and marking defect regions and defect types; adjusting the illumination uniformity and detail contrast of the citrus image by using a Retinex algorithm to form a Retinex enhanced image; introducing an attention mechanism of Transform, and performing feature extraction and attention modeling in combination with a convolutional neural network; predicting illumination distribution of the Retinex enhanced image through a deep learning model, and correcting illumination deviation; noise or artifacts in the Retinex enhanced image are removed, and an image after dynamic preprocessing is obtained; and selecting a deep learning model for defect detection, and training a defect detection model by using the dynamically preprocessed image. According to the method, the traditional advantages of image enhancement and the adaptive ability of deep learning are combined, an efficient and accurate solution is provided for citrus surface defect detection, the problems of uneven illumination, shadow interference and the like are effectively solved, and the robustness of defect detection is improved.

The invention relates to the technical field of image processing, in particular to a composite board explosive welding defect detection method based on an X-ray image, which comprises the following steps: acquiring an X-ray image of a composite board welding seam as a target image; and calculating a lead word tendency index of each pixel point in the target image, calculating a welding seam tendency index of the pixel point based on the lead word tendency index of the pixel point and the gray value of the pixel point, and calculating an input scale of each pixel point under each basic scale under a multi-scale Retinex algorithm by using the welding seam tendency index. According to the method, the target image is processed through the self-adaptive scale, so that the features of the welding seam area in the target image can be enhanced more effectively, meanwhile, the areas outside the welding seam are subjected to feature suppression, the influence of the areas outside the welding seam in the target image on subsequent defect detection is reduced, and the detection accuracy is improved. Therefore, the detection accuracy of the explosion welding defect of the composite board is improved.

The invention relates to the technical field of brake disc detection, in particular to an automobile brake disc defect detection method and system, and the method comprises the steps: obtaining the number and surface image of an automobile brake disc, and carrying out the image enhancement preprocessing of the surface image based on an improved Retinex algorithm; detecting and identifying the preprocessed surface image according to a preset improved AlexNet network model, and determining defect information on the automobile brake disc; marking the automobile brake disc according to the defect types and the number of the defect types so as to judge the quality of the automobile brake disc; according to the number of the automobile brake disc, the defect information and the mark of the automobile brake disc, determining whether the production line needs to be stopped for maintenance. According to the method, the overall contrast ratio of the surface image is improved, so that the local features of the surface image are more obvious, the improved AlexNet network model is adopted, so that the defect detection of the surface image can be more efficient and accurate, the production efficiency is improved, and the production cost is reduced.

The invention relates to the technical field of image processing, in particular to an identity recognition method and system based on image processing. When identity recognition is carried out, image illumination compensation operation is carried out on a collected image through a closed-loop process of semantic perception, type classification and mode-based compensation fusion, the type classification is divided into a low-light scene and an overexposure scene, and for the low-light scene, the overexposure scene is divided into an overexposure scene and an overexposure scene; for an overexposure scene, performing image enhancement processing on the pre-screened face image by adopting a face region distribution guided Retinex algorithm, and for the overexposure scene, pertinently adjusting cliplimit parameters of a CLAHE algorithm by sensing gradient features of a highlight region and a key texture region to realize image enhancement processing on the pre-screened face image; according to the invention, the method achieves the precise adaptation of the face structure, retains the illumination restoration of key details, solves the global defects of an existing method, and lays a high-quality image foundation for the subsequent shielding restoration and posture correction.

The invention provides a diamond wire saw steel ball detection method based on improved yov8. The diamond wire saw steel ball detection method comprises the following steps: S1, acquiring an original image of a diamond wire saw bead; labImage label images are used for marking beads, and data of a training set, a verification set and a test set are formed according to the proportion; s2, carrying out the preprocessing of the image, carrying out the zooming of the image, and carrying out the preprocessing of the image through employing an improved Retinex algorithm; s3, the yov8 model is improved, and an illumination invariant feature extractor (LIF) is added into the backbone; a CA attention mechanism is integrated in a C2f module, and a small target detection head is added and a large-scale detection head is deleted based on small bead size change, so that the small target detection head is used for detecting a bead small target; and S4, optimizing a loss function of the yolov8, improving the small target detection precision by using an improved IoU loss function, and training by using an improved yolov8 model to obtain a training result best.pt file.

The invention discloses a neurosurgery intraoperative lesion recognition system based on an ultrasonic image enhancement technology, and the system comprises an image collection module which is represented as a two-dimensional function I; the self-adaptive median filtering module is used for converting into an output image J; the gray level normalization module is used for generating an output image K; the multi-scale Retinex algorithm enhancement module is used for obtaining an enhanced output image E; the histogram equalization module is used for carrying out histogram equalization on the output image E; the feature extraction module is used for carrying out feature extraction based on a convolutional neural network CNN; and the classification module is used for obtaining an ultrasonic image at the focus. Therefore, noise can be suppressed through adaptive median filtering, contrast and definition can be enhanced through gray level normalization and histogram equalization, lesion features are highlighted through a multi-scale Retinex algorithm, and lesion features are accurately extracted and classified through combination of a convolutional neural network and a support vector machine.

The invention relates to the technical field of image processing, in particular to an automobile screw surface corrosion defect detection method, which comprises the following steps of: obtaining a suspected corrosion area according to the local gray scale feature of each pixel point in a gray scale image of a target screw; obtaining a first corrosion confidence coefficient according to the texture characteristics and the gray level distribution complexity of the suspected corrosion area; acquiring a second corrosion confidence coefficient according to the similarity between other suspected corrosion areas in other screws and the suspected corrosion areas and the edge ambiguity of the suspected corrosion areas; according to the first corrosion confidence coefficient and the second corrosion confidence coefficient, obtaining a self-adaptive Gaussian kernel of each pixel point in each suspected corrosion region, and according to the self-adaptive Gaussian kernel of each pixel point in each suspected corrosion region, enhancing the grayscale image by using a Retinex algorithm, and then performing corrosion detection on the target screw. And the detection accuracy of the rusted area on the surface of the automobile screw is improved.

The invention relates to a forest fire early warning method and system based on a Retinex algorithm, and belongs to the technical field of forest fire early warning. The method comprises the following steps: acquiring a low-illumination forest fire image; based on the Retinex theory, decomposing the image into a reflection image and an illumination image through a photosensitive decomposition module; constructing a weak light detection module, capturing weak light features in the illumination map by using a multi-scale convolutional layer, and generating a weak light attention feature map; constructing a weak light enhancement module, and combining the weak light attention feature map with the reflection map to generate a weak light enhanced illumination map; a low-illumination image reconstruction module is constructed, and a low-illumination reconstructed image is generated through element-by-element multiplication and residual connection in combination with the weak-light enhanced illumination image and the reflection image; and constructing a low-illumination image small sample classification module, segmenting the reconstructed image into feature blocks, extracting local information, comparing the local information with a database image, obtaining a recognition result, and determining whether to give an early warning or not. The recognition precision of the forest fire image can be improved.

The application provides a kind of underwater structure apparent disease identification method and system based on deep learning, wherein, method includes: according to the combination of fusion model and target recognition model, generate the pre-set underwater structure apparent disease identification model;Underwater apparent disease is identified by the pre-set underwater structure apparent disease identification model;The fusion model is built by improved CycleGAN model and multi-scale Retinex algorithm (MSR) network, for converting underwater image into clear and clear image with obvious features;The target recognition model is obtained by YOLOv5 model, for realizing the positioning and classification of underwater structure apparent disease.The present application solves the problems of inaccurate classification and low recognition accuracy of underwater structure apparent disease by the underwater structure apparent disease identification model, caused by factors such as camera imaging blur, insufficient contrast, dispersion and noise in complex water area.