676 results about "Low contrast" patented technology

The invention provides an underwater image enhancement method combining high-frequency enhancement, space-frequency mixed feature extraction and a Mama enhancement fusion technology. The method aims at solving the problems of low contrast ratio, color distortion, detail loss and the like caused by absorption and scattering effects in the light propagation process of the underwater image, the image quality is improved, and the visual perception effect is improved. The method specifically comprises the following four main steps: firstly, acquiring and preprocessing initial data sets of different underwater scenes; secondly, extracting texture detail information of the image through a texture enhancement module; thirdly, further enhancing texture details by using a space-frequency mixed feature extraction module; and finally, in combination with the original image, the texture information and the spatial frequency domain enhancement result, realizing feature fusion of the image through a Mama enhancement fusion module, thereby effectively recovering details of the underwater image, correcting color deviation and improving the definition and visual effect of the image. According to the method, the enhancement performance of the underwater image in a complex environment can be improved, and powerful support is provided for applications such as underwater detection, target detection, object recognition and image segmentation.

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

The invention discloses a liquid crystal display screen defect detection method, and relates to the technical field of liquid crystal display screen defect detection.A bidirectional polarization transmission structure light source is adopted to project 0-degree and 90-degree polarized light, interference of parasitic reflection on defect detection is reduced, and a self-adaptive exposure control unit is introduced; exposure parameters are adjusted in real time according to the light transmittance and reflection characteristics of the glass substrate, the imaging quality is optimized, and the detection effect is prevented from being affected by overexposure or underexposure; in the image processing stage, parasitic reflection information is extracted by combining a polarization difference algorithm of double-frame subtraction, high-frequency interference is removed by using fast Fourier transform (FFT), an optimized defect image after parasitic reflection suppression is obtained, real defect information is more prominent, and the defect contrast is improved; multi-scale Gaussian fitting filtering, a gradient lifting method and super-resolution reconstruction are adopted to enhance defect features in the image, so that a low-contrast defect area is easier to identify.

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

The invention discloses a sparse finite angle CBCT reconstruction method and system based on residual diffusion and a storage medium, and the method comprises the steps: carrying out the CBCT sparse finite angle scanning of a to-be-detected target, and obtaining sparse projection data; fDK reconstruction is carried out on the sparse projection data to obtain an initial CBCT image; generating a first optimized CBCT image from the initial CBCT image through an image pre-training network; through the first optimized CBCT image and the sparse projection data, using the trained residual diffusion model to determine a residual image of the to-be-detected target; summing the first optimized CBCT image of the to-be-detected target and the residual image of the to-be-detected target to obtain a second optimized CBCT image of the to-be-detected target, and the second optimized CBCT image is a final CBCT reconstruction image. According to the method, the problems of stripe artifacts and low-contrast tissue annihilation under limited angle scanning are solved, the large-view CBCT reconstruction resolution is improved, and the radiation dose is reduced.

The invention relates to an underwater image enhancement method based on wavelet Mama, which aims at the problems of color shift, low contrast ratio and fuzzy details of an underwater image, extracts shallow layer features of an underwater low-quality image, inputs the shallow layer features into a multi-scale coding network, and performs joint enhancement on low-frequency color information and high-frequency detail features by using a wavelet Mama unit. Global semantic features are obtained by combining down-sampling layer-by-layer compression, and global modeling of color offset correction and contrast enhancement is completed; the spatial resolution is recovered through up-sampling, and reconstruction and enhancement of texture details and color information are realized in combination with jump connection and a wavelet Mama unit; and finally, the features are mapped to an image domain and fused with input image residual errors, and an enhanced underwater image with natural color, clear texture and balanced contrast is generated. According to the method, frequency domain-space domain joint feature modeling is realized through the wavelet Mama unit, and the color authenticity, the structural definition and the detail perceptibility of the underwater image are remarkably improved.

The invention discloses a feature integration method for interactive convolution and dynamic focusing of infrared images. The feature integration method comprises the steps that infrared image pairs with different resolutions in various real scenes are obtained through an infrared camera; performing degradation preprocessing on a part of original high-resolution images to obtain low-quality high-resolution images to form a mixed low-resolution data set, and dividing the processed data set into a training set and a test set; constructing a double-layer feature extraction module for feature modeling; training a network by using the processed training set, and optimizing a loss function; and inputting a low-resolution infrared image into the trained network, and outputting a high-resolution reconstruction result. According to the method, local and global features are fused, so that the super-resolution reconstruction quality of the infrared image in complex scenes such as low contrast and fuzzy edges is remarkably improved, and meanwhile, relatively high calculation efficiency is kept.

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

The invention belongs to a target detection technology in the field of computer vision, and particularly relates to an X-ray security check image hazardous article detection system based on improved YOLOv11. Firstly, an X-ray security check image data set is divided into a training set and a test set according to the proportion of 6: 4, the system is based on a YOLOv11 architecture, a space and channel cooperative attention mechanism (SCSA) is introduced into a backbone network structure, the SCSA comprises two parts, namely sharable multi-semantic space attention (SMSA) and progressive channel self-attention (PCSA), and the SCSA is used as a main network of the system. The objective of the invention is to improve the detection performance of hazardous articles in an X-ray security inspection image, effectively enhance the understanding ability of a model for complex scenes, and improve the detection precision and speed of small targets, shielded objects and low-contrast hazardous articles. According to the method, the YOLOv11 algorithm is improved by combining the SCSA module, the detection precision and speed of the dangerous goods in the X-ray security check image are remarkably improved, and powerful technical support is provided for improving public safety and optimizing the security check process.

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

The invention provides a magnetic sheet surface defect detection method based on an improved target detection model. The method comprises the following steps: constructing a defect detection model based on a YOLO11n basic network model; the backbone network comprises at least one convolution module, at least one C3k2Her module combined with an edge refining structure and a layered edge refining structure, a spatial pyramid pooling fast module and a C2PSA module combined with a cross-stage local structure and a pyramid extrusion attention mechanism; the neck network is constructed based on a path aggregation feature pyramid network structure and has a multi-layer feature pyramid structure for processing a low-layer feature map to be fused to a higher-layer feature map, and the neck network further comprises at least one C3k2FFCM module combined with a cross-stage local structure and a fused Fourier convolution mixer module; training the defect detection model; performing defect detection; and outputting the surface defect information. According to the invention, the detection precision and efficiency of the surface of the magnetic sheet, especially small defects under low contrast and complex backgrounds, are improved.

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

The invention discloses a brain lesion nuclear magnetic image Transform diffusion segmentation method and a brain lesion nuclear magnetic image Transform diffusion segmentation system based on symmetry prior. The invention provides a novel prior-guided diffusion denoising brain lesion segmentation Transform model, long-distance coding features are extracted from an image, local information related to medical image prior features is supplemented, and frequency domain screening fusion coding components are further introduced. A U-shaped denoising model is designed based on a transform network structure, and in order to maintain accurate lesion-related semantic details, a prior feature coding branch is introduced. A priori feature coding branch considers modal-related voxel distribution characteristics and spatial symmetry in a modal, so that a priori guidance attention mask is learned, and attention weights of a pixel level and a patch level are dynamically adjusted. In order to eliminate image noise and a low contrast effect, in a decoding part, a three-branch wavelet frequency spectrum mixed attention module is designed for fusing and screening frequency spectrum feature embedding, and a stable segmentation result is further generated.

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

The invention discloses a wild animal target detection method based on improved YOLO11. According to the invention, double-dynamic convolution is designed to improve the feature extraction and nonlinear expression capability of the feature extraction module on the wild animal image so as to flexibly adapt to the feature difference between visible light and infrared images; a multi-scale expansion attention mechanism is introduced to improve the multi-scale target detection precision; unified-IoU loss is introduced to solve the problem of unbalanced quality of a prediction frame; and a DySample dynamic up-sampling operator is introduced, so that the detail recovery capability is improved, and the feature distortion is reduced. The problems that a traditional YOLO feature extraction module cannot effectively process an image shot by a field infrared camera, and an infrared image depends on thermal radiation, is low in contrast, weak in texture, large in environmental thermal noise and large in feature difference with a visible light image and is difficult to adapt are solved, and traditional CIoU loss easily pays attention to and fits a low-quality prediction frame. And a sampling module cannot effectively reconstruct low-resolution infrared image features.

The invention provides a mobile phone glass quality detection method and system based on image detection, and relates to the technical field of image defect detection, and the method comprises the steps: configuring an imaging condition for collecting a mobile phone glass image, and collecting an original image of mobile phone glass according to the configured imaging condition; constructing a transparency enhancement algorithm according to a transparent background existing in the collected original image, and processing the original image according to the transparency enhancement algorithm to obtain an enhanced image; a crack boundary self-enhancement step is introduced, micro cracks and fuzzy scratches existing in an enhanced image are positioned, the enhanced image is marked according to the positioning result of the micro cracks and the fuzzy scratches, and a marked image is obtained. The phenomenon of false detection or missing detection easily occurring in mobile phone glass detection under a transparent background in related technologies is solved through a transparency enhancement algorithm; and the problem that boundary fuzzy defects such as microscopic cracks and fuzzy scratches with extremely low contrast are difficult to identify is solved through the crack boundary self-enhancement step.

The invention belongs to the technical field of saliency target detection, and particularly relates to a saliency target detection method based on edge perception and an attention mechanism. The invention provides a saliency target detection method based on edge perception and a dual-channel attention mechanism, and aims to solve the problems that most of conventional saliency target detection methods are low-resolution image processing, high-resolution image samples are scarce, accurate detection is difficult and the like. By introducing an edge sensing module, target boundary information is accurately extracted, and the structural features of an image are enhanced, so that the target detection precision under low contrast and complex backgrounds is effectively improved; through a double-channel attention mechanism, features are weighted in a spatial domain and a channel domain, a salient region is adaptively focused, background interference is suppressed, and the robustness and accuracy of a detection model are improved. The method can effectively adapt to a saliency target detection task in a complex scene, and meets the actual engineering requirements.

The invention relates to the technical field of image data processing, in particular to a method for detecting the surface quality of a mining anchor cable steel strand after stabilization treatment, and the method comprises the steps: obtaining a surface image of a to-be-detected steel strand; determining a local texture collaboration degree index of each pixel point; determining a longitudinal consistency deviation index of each pixel point; determining a defect significance index of each pixel point; and identifying a defect area in the surface image of the to-be-detected steel strand based on the defect saliency index of each pixel point. According to the method, a local texture synergy degree and longitudinal consistency deviation index is constructed, and the gray change characteristics and the spatial change rate are combined, so that multi-dimensional quantitative detection of small defects on the surface of the steel strand is realized, noise and real defects are effectively distinguished, the sensitivity to low-contrast defects is improved, missing detection and false alarm are reduced, and the detection accuracy is improved. And a high-reliability quality detection method is provided for the mining anchor cable steel strand.

The invention belongs to the technical field of image processing, relates to a multi-scale road crack detection method and device in extreme weather and a medium, and relates to the technical field of image processing, and the method comprises the steps: constructing an extreme weather data set; constructing a target detection network; training a target detection network by adopting the data set to obtain a target recognition model; according to the method, the lightweight backbone network CAPNet is adopted to replace a backbone network of the RT-DETR, and the feature extraction efficiency is improved; a cascade multi-branch feature fusion module is used for replacing a CCFM module in a traditional neck network, dynamic integration of multi-scale features is achieved, a multi-scale edge enhancement module is additionally arranged in a network structure, the perception ability of the model to crack edges of different scales is enhanced, the feature expression problem under the low contrast and blurred image conditions is effectively solved, and the dynamic integration of the multi-scale features is achieved. And the balance between the detection precision and the efficiency is realized.

The invention relates to the technical field of tunnel crack detection and artificial intelligence edge calculation, and provides a tunnel apparent disease detection method based on deep learning and knowledge distillation, which comprises the following steps: step 1, introducing spectral domain information enhancement to an original tunnel image, the edge texture features of the disease area in the image are enhanced through methods such as multi-scale wavelet transform and small-scale enhancement. Step 2, constructing a high-performance teacher model, introducing a flexible up-sampling structure to adapt to feature recovery requirements of different levels of semantic information, introducing an efficient visual coding module to enhance feature fusion capability of different scale channels, and designing a scale adaptive weighted loss function at the same time; by introducing a frequency spectrum enhancement mechanism, structural features of disease areas with low contrast, fuzzy edges and the like are remarkably enhanced in an image preprocessing stage, clearer information input is provided for a model, and the stable recognition capability of a system in environments of uneven illumination, complex background and the like is enhanced.

The invention relates to the technical field, in particular to an offline handwritten mathematical formula recognition method based on deep learning, which comprises the steps of data acquisition and processing, formula detection and symbol segmentation, symbol feature extraction and recognition, structural analysis and reconstruction and evaluation and optimization. According to the offline handwritten mathematical formula recognition method based on deep learning, the robustness of a model to partial shielding is improved through decomposition and recombination, real handwritten deformation is simulated through local distortion, the generalization ability is enhanced through global distortion, a low-contrast image is improved according to CLAHE equalization, the illumination influence is reduced, and formula data enhancement diversification processing is ensured; a formula area is positioned through YOLOv5, adhesion symbols are accurately segmented and processed according to U-Net, global features are extracted by adopting Transform, a Tesseract OCR is called, a symbol library is self-defined, the OCR and a rule engine are combined, the special symbol recognition rate can be increased, a syntax tree is generated through syntax tree and PCFG analysis and recursive descent, an optimal structure is selected, and a two-dimensional layout is accurately analyzed.

The invention provides a medical image segmentation method and system based on spatial detail enhanced vision, and relates to the technical field of image processing. The method comprises the following steps: performing initial feature mapping on an input and preprocessed medical image to obtain an embedded feature map; the embedded feature map is input into an encoder for feature extraction, and multi-scale features are obtained; the encoder comprises a plurality of encoding stages, and the number of channels is doubled and the spatial resolution is halved through down-sampling operation between the encoding stages; the multi-scale features are subjected to up-sampling and spatial enhancement reconstruction step by step through a decoder, and a high-precision segmentation result is generated; wherein the decoder comprises a plurality of decoding stages, the decoding stages correspond to the encoding stages, and feature fusion is carried out between the corresponding stages of the encoder and the decoder through jump connection. According to the method, the boundary description precision and the segmentation robustness of the low-contrast image can be improved without increasing the linear complexity, and the method is suitable for medical image segmentation scenes of skin lesions, gastrointestinal polyps and the like.

The invention provides an infrared small target detection method based on frequency domain decomposition, and aims at solving the problem that a traditional spatial domain method is insufficient in detection performance under the conditions of complex background, low contrast and noise interference. Aiming at the problem that an infrared small target generally has relatively small spatial distribution and high-frequency characteristics, an input image is decomposed into high-frequency and low-frequency components through a self-adaptive frequency domain decomposition technology, frequency domain information perception and a spatial information aggregation module are combined, and frequency domain characteristics are weighted and extracted by adopting a sparse attention mechanism; the contributions of different frequency components are dynamically adjusted. Through efficient feature fusion, the expression of target information is optimized, and especially in a low-contrast and complex background environment, the detection precision and robustness are improved. An experimental verification result on an NUAA data set shows that compared with an existing mainstream method, the method has remarkable advantages on multiple evaluation indexes, and the effectiveness and superiority of the method in infrared small target detection are verified.

The invention relates to the technical field of biomedical engineering and digital image processing, in particular to a digital image enhancement method based on cytopathology. The method comprises the following steps: eliminating noise and artifacts caused by non-uniform dyeing, section folding and dust factors; the problem of dyeing difference caused by different scanning devices is solved; key areas including cell nucleuses and cell membranes are highlighted, so that the visual effect of a low-contrast image is improved; segmenting an area, including cell nucleuses and cytoplasm, of the cells, and extracting morphological characteristics; the detail resolution of a low-resolution image, especially the definition of a small cell structure, is improved; the brightness component Y is corrected to compensate for the difference of different dyeing methods and dyeing qualities in brightness response, then the chromatic value is adjusted according to the color correction factor to make the colors of the images more consistent, and finally, the images which are acquired by different dyeing methods and dyeing qualities and subjected to brightness and chroma correction are fused to make the color of the images more consistent. And a final corrected image is obtained.

The invention discloses an impregnated paper surface uniformity detection method based on texture image analysis, and particularly relates to the field of image enhancement and texture analysis, and the method comprises the steps: obtaining an impregnated paper image, constructing an edge image based on a gray difference value, extracting an image region with an edge value lower than a first threshold value in the edge image, and defining the image region as a fuzzy region; and edge value difference calculation between adjacent pixels is carried out on the fuzzy region to form a difference chart, and a pixel region with an edge change value greater than a second threshold value is extracted from the difference chart and is defined as a fracture region. According to the scheme, the weak boundary between the adhesive film and the fiber is extracted by recognizing the fuzzy region with the low edge value and tracking the direction path of the fuzzy region, the problem that the low-contrast edge cannot be recognized through a traditional method is solved, and the detection precision of the non-uniform region is improved.

The invention discloses a remote sensing image target segmentation method and network based on expansion multi-scale fusion, and belongs to the technical field of remote sensing image processing, and the method comprises the steps: carrying out the preprocessing and feature map extraction of an original remote sensing image; processing the extracted feature map by using expansion convolution, an attention mechanism and an attention matrix to obtain an enhanced feature map; and performing multi-scale feature extraction and fusion on the enhanced feature map, and outputting a target segmentation result with the same resolution as the original remote sensing image through a decoder. Through combination of expansion convolution and multi-scale feature fusion, the method can effectively improve the segmentation precision of the target region in the remote sensing image, especially in a complex scene and a low-contrast region. And by applying an efficient attention mechanism and feature enhancement in the feature extraction process, the performance of the segmentation model is further optimized, so that the segmentation model can be efficiently operated under different computing resources, and the method has a wide application prospect.

The invention discloses a colorectal cancer focus segmentation method based on improved TransUNet. The method comprises the following steps: collecting related pathological image data of a colorectal cancer patient; enhancing and expanding the data set by adopting a data enhancement technology, and adjusting and processing the image data; the method comprises the following steps: constructing a model, integrating a PEMA module at multiple positions of the model, introducing an EUCB up-sampling module into a decoder part, replacing standard convolution of the decoder part with lightweight dynamic convolution, and using a composite loss function MediBoundFusion Loss; the preprocessed training data set is input into the improved TransUNet network model to be trained; and the colorectal cancer focus is segmented by adopting the improved TransUNet network model after training is completed. The key problems that focus features are fuzzy, boundaries are difficult to define, forms are irregular, and effective features are difficult to extract due to low contrast of early cancerous tissues can be solved.

The invention discloses a camouflage target detection method and system based on a double-domain fusion enhanced network, and relates to the technical field of target detection. Through the nonlinear double-domain fusion module, in combination with nonlinear mapping of a spatial domain and a frequency domain, key difference characteristics of a frequency domain amplitude spectrum and a phase spectrum are captured, the problem that the detection performance is reduced in a scene of low contrast and the like depending on an RGB spatial domain is solved, and the target discrimination degree is improved; based on a lightweight scale perception modulation converter and a double-feature fusion module, multi-scale features are extracted, aligned and fused, a semantic relation is integrated by means of cross attention, and the problems of detail loss and boundary fuzziness caused by scale diversity are solved; the context feature enhancement module integrates cross attention and edge auxiliary injection, accumulates multi-layer feature integration, gives consideration to a global boundary and a local structure, effectively reduces false detection, missing detection and edge roughness, and further enhances robustness through multi-layer auxiliary supervision.

The invention relates to a polylactic acid test piece internal defect detection method, and belongs to the technical field of target detection, the method comprises the following steps: S1, image processing to obtain an input image; s2, attention enhancement feature extraction: preliminarily extracting spatial features, capturing global information and local information, focusing a defect area, and generating an enhancement feature map; s3, carrying out feature fusion, and outputting a fused feature map; s4, performing self-attention re-parameterization, and outputting a local feature map; s5, HSV spot detection including a color conversion algorithm and a defect enhancement algorithm; and S6, outputting a result, establishing a bounding box regression task, predicting a frame coordinate, outputting a defect position, identifying a category label, outputting a defect type, and evaluating reliability and outputting confidence. According to the method, feature extraction is carried out by using depth separable convolution, the visibility and detectability of low-contrast defects are enhanced, and the attention enhancement network is established to adaptively pay attention to key areas related to the defects, so that defect detection is more accurate and efficient.

An infrared ship target detection method based on synthetic fog and multi-level feature fusion and enhancement comprises the following steps: S1, data set preparation: simulating an infrared ship target in a fog environment by using an atmospheric scattering model, and constructing a fog environment infrared ship data set; s2, adding a small target detection layer, and reconstructing a neck network to improve the shallow feature weight; s3, a C2f-DynamicConv structure is designed, and the C2f structure of the YOLOv8n backbone network is replaced by the C2f structure of the YOLOv8n backbone network; s4, designing a multi-level feature fusion and enhancement (MLFFAE) module, and adding the MLFFAE module between the feature extraction and the feature fusion of the YOLOv8n model, and carrying out the feature extraction and the feature fusion of the YOLOv8n model; s5, designing a lightweight shared convolution detection head to improve an original YOLOv8n detection head; and S6, training and testing the data set by using the improved IB-YOLO model. The method solves the problems that in the fog environment, the infrared ship imaging quality is reduced, the image is blurred, the ship target feature texture is not obvious, and the infrared ship image has the defects of low resolution, low contrast ratio and the like, so that the detection performance is poor.