336 results about "Saliency map" patented technology

The invention discloses an optical remote sensing image salient target detection method based on progressive attention enhancement, and belongs to the technical field of computer vision. The method comprises the following steps: preprocessing an original data set; inputting the preprocessed image into a hierarchical progressive fusion encoder, capturing a global irregular topological structure and local fine-grained image details, and realizing cross-hierarchical feature fusion; inputting the output characteristics of the encoder into a global context enhancement module, and capturing multi-level context information by adopting a parallel multi-branch structure; and inputting the output features of the hierarchical progressive fusion encoder and the global context enhancement module into a multi-scale progressive attention enhancement decoder, carrying out hierarchical decoding on the input features by adopting a saliency-guided attention mechanism, and gradually aggregating deep semantic information and shallow detail features to realize coarse-to-fine progressive optimization, so as to improve the robustness of the multi-scale progressive attention enhancement decoder. And finally generating a saliency map. The method can effectively improve the processing performance of an irregular topological structure and a complex context relationship in the optical remote sensing image.

The invention relates to the technical field of industrial machine vision detection and image processing, in particular to an automobile part production mold surface smoothness detection system based on image enhancement, which comprises a data acquisition module used for acquiring an original grayscale image of the surface of an automobile part mold; performing low-pass filtering processing on the original grayscale image to eliminate imaging thermal noise; the manifold reconstruction module is used for constructing a structure tensor field; reversely deducing a pseudo-curvature field of the mold surface; the adaptive enhancement module is used for generating a corrected image; constructing a texture orthotropic diffusion model; generating a texture reconstruction reference image; the surface metering module is used for calculating the difference between the corrected image and the texture reconstruction reference image and generating a defect saliency image; calculating the surface roughness value of the mold surface; according to the method, the problem that design textures and abnormal scratches are difficult to distinguish in the prior art is effectively solved, and the technical bottleneck that micro defects are easily missed in a complex geometric structure in traditional visual detection is overcome.

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

The invention relates to the technical field of image processing, in particular to a high-voltage electrical equipment surface defect identification method based on image processing. The method comprises the following steps: analyzing the gradient of pixel points in a to-be-analyzed image of a to-be-detected area on the surface of the high-voltage electrical equipment to obtain the weight of each pixel point; weighting the gray value of each pixel point in the to-be-analyzed image subjected to Laplacian filtering by using the weight of each pixel point of the to-be-analyzed image to obtain a first feature map; calculating a local standard deviation of each pixel point in the to-be-analyzed image so as to obtain a first parameter and a second parameter; constructing two Gaussian kernels based on the first parameter and the second parameter to filter the to-be-analyzed image to obtain a second feature map; fusing the first feature map and the second feature map of the to-be-analyzed image to obtain a defect saliency map of the to-be-analyzed image; and recognizing a surface defect area of the high-voltage electrical equipment based on the defect saliency map of each to-be-analyzed image. According to the invention, the accuracy of high-voltage electrical equipment surface defect identification can be improved.

The invention discloses an unmanned aerial vehicle outdoor inspection method based on multi-modal fusion, and the method comprises the following steps: obtaining the inspection data of an unmanned aerial vehicle, and carrying out the feature extraction and space-time alignment; calling a GNPDE algorithm, and constructing a multi-modal field mapping layer; constructing a structure-thermal field joint graph structure, and establishing a double-branch coupling solution structure; constructing a topological adaptive edge weight adjustment module, and dynamically modulating the edge weight by adopting a physical modulation function; introducing an energy conservation constraint layer, and executing constraint solution; a multi-scale PDE evolution algorithm subset is quoted and combined, and a scale weight sharing mechanism is adopted to complete cross-scale joint optimization; constructing an abnormal residual reasoning module, and generating an abnormal significance map; and performing spatial registration and superposition on the abnormal saliency map and unmanned aerial vehicle inspection data, and outputting an equipment-level inspection report and a risk level conclusion. According to the invention, the inspection abnormity identification precision and the structure-thermal field reasoning stability are improved.

The invention discloses an oil and gas pipeline magnetic flux leakage image defect identification method based on a deep attention mechanism, relates to the technical field of oil and gas pipeline detection, and is used for solving the problem of inaccurate identification of a magnetic flux leakage image of a pipeline elbow section. According to the method, the image frame sequence with the posture annotation is constructed through unified time reference and space coordinate mapping, and accurate alignment of the image and the pipeline position is achieved; a structural area marking graph and non-rigid normalization are introduced to compensate the distortion of the elbow section, and the image consistency is improved; constructing a structure perception embedded image on the distortion compensation image, fusing position, gradient and texture features to implement feature propagation and attention guidance, and generating a feature saliency map; a defect area is accurately extracted in combination with layered reconstruction and a two-stage judgment strategy; through continuous frame monitoring and recognition stability judgment, recognition parameters are adaptively updated, a closed loop from recognition to updating to verification is constructed, and the precision of oil and gas pipeline magnetic flux leakage image recognition under complex working conditions is enhanced.

The invention relates to the field of image processing, in particular to a box-type substation spraying quality detection method based on image processing, and the method comprises the steps: firstly obtaining a to-be-detected image, and calculating a pixel point structure tensor component after the to-be-detected image is preprocessed; extracting a gradient energy field and a gradient main direction angle field based on the component, and further calculating gradient radial consistency representing geometric characteristics of the defect; in combination with the to-be-detected image and the gradient energy field, respectively calculating saturation and flatness indexes representing optical reflection characteristics and intensity-gradient cross-correlation representing defect spatial forms; fusing the three features to generate a final saliency map; and finally, carrying out threshold segmentation and post-processing on the saliency map to obtain a detection result. According to the method, the physical spraying defect and the optical highlight reflection can be effectively distinguished, and the defect detection accuracy and robustness are remarkably improved.

The invention relates to the technical field of industrial vision, in particular to an aviation part crack detection and repair method which comprises the following steps: acquiring an aviation part optical image at a reference time point and an aviation part optical image at a to-be-detected time point; according to the method, logarithmic polar coordinate transformation is carried out on different time point images, rotation, scaling and translation parameters are extracted, an image registration relation is established, the structural consistency of time sequence images in a local area is enhanced, a structural tensor is constructed for each pixel neighborhood, the change characteristics of the dual-time-phase tensor are compared, a structural change saliency map is generated, and the structural change saliency map is obtained. Sensitive capture of a tiny deformation area is achieved, the responsiveness to an initial crack is improved, then a crack propagation interval is further refined into a main crack path in a self-adaptive threshold segmentation and skeleton extraction mode, the tip acutance of the crack is calculated in combination with tip contour information of a geometric boundary of the crack, and the initial crack is obtained. And quantitative support is provided for the crack danger degree.

The invention provides an electric power line defect detection method and system based on visual identification, and relates to the technical field of line detection.The method comprises the steps that firstly, a visible light and infrared image dual-light registration and differential operation technology is adopted, and a fusion feature map capable of reflecting component thermal anomaly and material difference at the same time is generated; secondly, a black box type target detection model is abandoned in a part positioning link, but line segment screening and reconstruction are carried out by combining probability Hough transform with specific prior geometric knowledge of a power line, so that dependence on a large amount of labeled data is reduced, interpretability of a positioning process is enhanced, and the positioning accuracy is improved; according to the method, accurate areas of key components such as wire insulators can be extracted in a complex background, the concept of a probability saliency map is introduced in a defect identification core link, so that a defect area is enhanced and highlighted, and then a complete defect contour is determined by adopting an adaptive threshold segmentation and area growing algorithm; accurate mapping from pixel-level features to object-level defects is realized.

The invention discloses a self-adaptive diffusion image editing method and system based on concept attention, and the method comprises the following steps: constructing a paired data set; analyzing the editing instruction, and extracting a key concept; a pre-trained T5 language model is utilized to convert the key concept into text embedding, and the text embedding is mapped to an image feature space; modifying a diffusion model based on a Transform architecture, embedding a concept attention module in an attention layer of a multi-modal diffusion converter, calculating an attention score between image features and concept embedding, and generating a concept saliency map; in the denoising process, the weight of the target area is adjusted by using the concept saliency map so as to realize accurate editing. According to the method, under the condition that the global image quality is not affected, the editing precision can be improved, interference to a non-target area is reduced, and meanwhile, reinforcement learning and real-time feedback are combined, so that the model can be adaptively optimized, and an editing result better meeting the user requirement is generated.

The invention discloses an electronic component packaging defect detection method and system based on image acquisition, and relates to the field of image analysis, and the method comprises the steps: collecting a multi-mode image of a to-be-detected electronic component package; pixel alignment is carried out on the multi-modal image, the multi-modal image after pixel alignment is used as an R channel, a G channel and a B channel to be stacked, and a three-channel pseudo-color image is generated; inputting the three-channel pseudo-color image into a pre-trained convolutional auto-encoder model to obtain a reconstructed three-channel image; analyzing the difference between the three-channel pseudo-color image and the reconstructed three-channel image to obtain an analysis result, and generating a three-channel residual image according to the analysis result; generating a single-channel defect saliency map based on the three-channel residual image; and when a connected region of which the pixel value exceeds a preset pixel threshold value exists in the single-channel defect saliency map, judging that the connected region is a real defect. The detection false alarm rate can be effectively reduced, and the production efficiency is improved.

The invention belongs to the technical field of image processing, and particularly relates to a casting surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a gray-scale image and a brightness background image of a vortex casting to be detected, and calculating the illumination deviation of each position in the image; segmenting the grey-scale map of the vortex casting to be detected by using an Otsu method to obtain a binary image; constructing a circular structural element according to the width of the optical interference region and the resolution of the imaging equipment; performing corrosion operation on the binary image based on the structural elements to generate a structural saliency image; obtaining a defect saliency value by using a product of a gray value and an illumination deviation in the structure saliency map; and calculating a segmentation threshold value based on the statistical characteristics of the defect-free sample, and identifying the position of which the defect saliency value is greater than the segmentation threshold value as a defect point. Through physical constraint and illumination feature fusion, the problem that a dark vortex structure and a real defect are difficult to distinguish is effectively solved, and the detection precision is improved.

The invention discloses an infrared super-resolution method based on visible light and infrared image fusion. The infrared super-resolution method comprises the following steps: acquiring and registering a high-resolution visible light image and a low-resolution infrared image; preprocessing the visible light image to extract a detail base and a texture saliency map; performing edge keeping smoothing processing on the infrared image by using the visible light image as guidance; the method comprises the following steps of: establishing a multi-factor decision model which depends on infrared thermal intensity, visible light texture significance and cross-modal edge consistency to calculate a pixel-level adaptive fusion weight; and finally, carrying out weighted fusion. Through an intelligent and content-adaptive fusion strategy, artifacts are effectively suppressed, the detail expressive force and the overall quality of the infrared image are remarkably improved while key thermal information is kept in a lossless manner, and the method is low in calculation cost and suitable for a real-time system.

The invention discloses an automatic driving obstacle recognition method for a complex scenic spot road scene. The method comprises the following steps: acquiring an original image of a scenic spot road; inputting a multi-scale feature extraction network to generate a multi-scale feature map; inputting the image into a multi-branch decoder to respectively obtain an obstacle region segmentation result, an obstacle position bounding box coordinate and an obstacle type classification probability; in combination with surrounding environment information of road geometrical morphology, weather and signal lamp states, feature weighting is carried out on the surrounding environment information by adopting an attention mechanism, and an initial obstacle saliency map is generated; according to a preset obstacle type priority level, detecting and processing an identification result of spatial overlapping in the image to obtain an obstacle identification result after conflict resolution; and performing spatial consistency proofreading on the road path segmentation map and the road path segmentation map, eliminating inter-frame jump based on time sequence smoothing, and finally outputting an identification result. According to the invention, the problem of high-precision and real-time obstacle recognition for complex scenic spot road scenes is effectively solved.

The invention provides an image processing method, device and equipment based on hardware perception and dynamic rarefaction, and relates to the technical field of image processing, and the method comprises the steps: inputting hardware operation state data into a preset PPO strategy network, so as to obtain the sparse rate of each convolution layer; reserving a specified number of channels as effective calculation channels; performing saliency region analysis on an input to-be-processed image to generate a pixel-level saliency map corresponding to the to-be-processed image; dividing the to-be-processed image into a key target area and a background area; allocating a first preset precision to the key target area for image processing, and allocating a second preset precision to the background area for image processing; according to the sparse rate of each convolutional layer, the first preset precision allocated to the key target region and the second preset precision allocated to the background region, collaborative image processing is carried out on the to-be-processed image; according to the invention, the large-scale landing capability and continuous optimization performance of the edge intelligent vision system can be improved.

The invention discloses a multi-scale self-distilled underwater acoustic signal identification method and system based on saliency mask modeling, and belongs to the field of artificial intelligence and underwater acoustic signal processing. Comprising the following steps: (1) introducing a saliency mask mechanism to construct a self-supervised contrast learning framework for pre-training, and shielding a key information region in a time-frequency domain through an attention-guided saliency map mask mechanism; (2) designing a multi-scale self-distillation architecture, and aligning global and local information of the weak target on category and block mark levels through global and local distillation mechanisms; and (3) pixel-level reconstruction loss is introduced for collaborative optimization, and joint optimization of semantic perception and fine-grained feature modeling is realized by recovering signal details of a masking area. And (4) a fine tuning stage: only training a classification head by freezing the backbone network. According to the method, robust and high-generalization-ability audio representation can be obtained without depending on large-scale annotation data, and the accuracy and adaptability of underwater sound signal recognition are improved.

The invention relates to the technical field of image processing, in particular to an aluminum alloy die casting surface defect detection method based on machine vision. The method comprises the steps of obtaining a grayscale image of the surface of a die casting; constructing a Gaussian scale space of the grayscale image, and obtaining scale images under a plurality of scales; calculating a defect saliency index corresponding to each pixel point in the grayscale image based on the multi-scale image, and generating a defect saliency map; and performing threshold segmentation on the defect saliency map to obtain a segmented image, performing morphological processing identification on the segmented image, and determining the position of a surface defect area. According to the invention, missed detection and false detection in the surface detection process of the aluminum alloy die casting can be reduced.

The invention discloses a dam micro-variation response identification method and system based on multi-scale information reconstruction, the method fuses multi-source heterogeneous data such as images, strain, displacement, acoustic emission and the like, adopts a time axis alignment and spatial registration strategy to construct standardized input, enhances disturbance feature expression based on wavelet transform and a significance attention mechanism, and improves the accuracy of dam micro-variation response identification. The method comprises the following steps: constructing a small-scale disturbance modeling module by introducing expansion convolution and a spatial saliency map, carrying out micro-variation trend identification and risk assessment in combination with a Transform model, carrying out lightweight compression on the model and then deploying the model at an edge terminal, realizing local rapid identification and network disconnection alarm, supporting model hot update and structure health trend analysis in cooperation with a cloud platform, and improving the reliability of the model. The method has the advantages of being high in recognition sensitivity, high in anti-interference performance, wide in deployment adaptability and the like, and is suitable for early-stage anomaly detection and intelligent safety supervision of multiple types of dam structures.

The invention relates to the technical field of machine vision detection, in particular to an automobile part surface defect online detection method and system. The method comprises the following steps: acquiring a grayscale image, calculating the size of a structural element for each pixel based on a local Gaussian Laplacian operator response variance, filtering to obtain a substrate image according to the size of the structural element, and differentiating to obtain a texture image. Determining a Gabor scale and a gray-level co-occurrence matrix statistical direction by using the size, and extracting a cooperative direction gray-level co-occurrence matrix feature; and a weight is set based on the size and is subjected to weighted fusion with a multi-scale rotation invariant local binary pattern feature to generate a texture saliency map, and texture defects are judged. On the substrate image, taking the gray value as the height, and determining a neighborhood calculation curvature feature based on the size to detect the substrate defect. According to the scheme, the image scale can be adaptively analyzed, the background texture is effectively inhibited, and therefore different types of tiny defects such as scratches and pits can be reliably detected.

The present application provides an information processing device and method, and a computer-readable storage medium. The information processing device comprises a processing circuit which is configured to: generate a saliency map of a sample image on the basis of a predetermined model which processes a task for the sample image, wherein the saliency map reflects the degree of attention to objects at different positions in the sample image when the predetermined model processes the task; and adjust, on the basis of the saliency map and the labeling area in the sample image, the parameters of the image signal processor which generates the sample image, such that the difference between the task processing result and the labeling value of the sample image meets a preset condition.

The invention relates to the technical field of computer vision, in particular to a fabric color fastness analysis method and system based on image processing. Comprising the following steps: acquiring a multispectral image of a to-be-detected fabric, and synchronously acquiring multi-factor data; processing the multispectral image to generate an enhanced image; performing image recognition on the gray sample card in the enhanced image to generate a calibration image; performing spectral data projection on the calibration image through a dimension reduction algorithm, and performing color analysis and quantification on color change to generate color difference features; extracting texture features by using a conditional generative adversarial network, carrying out image recognition and calculation on the calibration image through a decoupling algorithm, and generating a region credibility graph; and inputting the chromatic aberration features, the texture features, the multi-factor data and the regional credibility map into an image feature fusion model for mapping, performing analysis through gradient visualization, and outputting an abnormal feature saliency map. According to the method, a color fastness objective analysis closed loop is created, so that the accuracy and the universality of an analysis result are improved.

The invention discloses a remote sensing image segmentation method based on double-sequence significance guidance and space gating, and belongs to the field of computer vision. According to the method, firstly, a training data set is constructed, features are extracted through a double-branch encoder, the features comprise a global sequence scanning branch and a saliency spiral scanning branch, the global sequence scanning branch adopts horizontal and vertical forward and backward scanning to reserve global layout information, and the saliency spiral scanning branch positions an initial anchor point through a saliency map to generate a bidirectional spiral path and focus key area features. In the encoder, deep fusion of the convolutional neural network and the Mama model is realized through a space gating state transition mechanism, and the Mama hidden state is guided to be updated by using space features. The decoder completes double-branch feature calibration and alignment through a multi-stage cross-scanning feature calibration module, reinforces semantic consensus and optimizes detail differences, and adopts a three-head supervision and consistency constraint strategy combined training model. According to the method, the segmentation precision and efficiency of the high-resolution remote sensing image complex ground feature are remarkably improved.

Systems and methods for automatically extracting one or more salient images from a surgical video stream are described. A plurality of records including annotated images from recorded surgical procedures are used as training data to generate an image extraction machine learning model. Features, extracted from the training data, are used as inputs to the image extraction machine learning model in a training phase, which outputs salient images. After training, features extracted from the surgical video stream are input into the trained image extraction machine learning model to output the one or more salient images from the surgical video stream.

The invention relates to a scratch detection method, device and equipment for a transparent film and a medium, and the method comprises the steps: firstly carrying out image multiplication processing and dynamic range mapping on a transparent film gray level image, and obtaining a target gray level image through threshold segmentation; calculating a pixel gradient magnitude based on the image, synthesizing an edge gradient image, and generating a first salient image through Gaussian filtering and gray linear transformation; meanwhile, logarithmic transformation is carried out on the original target image to obtain a second salient image; secondly, respectively calculating gray average values of the two salient images, determining a self-adaptive segmentation threshold by combining a preset threshold, extracting a defect region through double-image threshold segmentation, and obtaining an intersection to obtain an initial scratch region; and finally, screening according to a preset area condition to obtain a final scratch area. According to the method, through the multi-feature fusion and self-adaptive threshold technology, the problems of low contrast, uneven illumination and the like in scratch detection of the transparent film are effectively solved, the detection efficiency and accuracy are remarkably improved, and the method has high engineering application value.

The invention relates to the technical field of agricultural intelligent detection, in particular to a blueberry tree pest and disease damage detection method based on machine vision. The method comprises the following steps: acquiring an ultraviolet gray image and a visible light color image of the same blueberry target, extracting a local gray extreme point of a fruit powder layer in the ultraviolet image as an anchoring node, and constructing a cluster manifold topology network for describing fruit space distribution; and taking the topological network as a deformation control skeleton, and carrying out pixel-level non-rigid registration on the visible light image by utilizing a thin-plate spline interpolation function. And then, performing weighted difference operation on the registered image and the ultraviolet image to generate a spectral residual saliency map which inhibits a healthy fruit powder background and highlights an abnormal region, and extracting a pest and disease damage target through threshold segmentation. According to the method, the problem of cross-modal registration of dense small fruit clusters in a dynamic wind blowing environment is effectively solved, fruit powder interference is accurately eliminated by utilizing spectral physical characteristics, and the detection robustness is remarkably improved.

Aspects presented herein may enable an extended reality (XR) headset (e.g., a UE) to use statistics and / or a saliency map to dynamically determine the number of foveation levels to be used for a display. In one aspect, a UE estimates a detail level specified by a periphery region of a display based on scene statistics and / or saliency. The UE determines a first set of foveation levels or a second set of foveation levels to be applied to the display based on the estimated detail level. The UE switches, based on the determination, to the first set of foveation levels or the second set of foveation levels if the display is applying a different set of foveation levels. The UE outputs a set of images or videos via the display based on the first or the second set of foveation levels.

The invention relates to the field of image processing, in particular to a fish and shrimp denoising enhancement and intelligent identification method based on a two-dimensional sonar image, which comprises the following steps: extracting a sonar image water background, dividing regions and calculating a signal-to-noise ratio, dynamically adjusting a noise threshold, and denoising to obtain a salient image. And a multi-rotation-angle convolution kernel set is constructed based on a size statistics preset ellipse parameter. And clustering salient image pixel points, setting a bounding rectangle as a suspected target area, and judging the consistency of continuous frame targets through an optical flow algorithm. And dynamically adjusting a convolution kernel weight enhancement image, training a YOLO model to recognize a target, superposing recognition results and providing statistical information. According to the method, the fish and shrimp targets with variable sizes and directions in the sonar image are accurately identified and tracked by utilizing dynamic noise threshold adjustment and a multi-angle convolution kernel set in combination with a denoising enhancement technology based on a signal-to-noise ratio and an optical flow algorithm, the limitation of a traditional method is effectively overcome, and reliable technical support is provided for target detection in a complex underwater environment.

The invention discloses a saliency-guided time sequence adversarial sample generation method and a saliency-guided time sequence adversarial sample generation system, and the method comprises the steps: obtaining a target time sequence classification model and a corresponding original input sample, carrying out the supervised training of the model through a time sequence training set, so as to guarantee the prediction accuracy of the model for the original input sample, and setting a disturbance iteration parameter; generating a saliency map of the input sample based on the target time sequence classification model; constructing a composite loss function fusing classification loss and saliency alignment loss for the target time sequence classification model based on the saliency map, carrying out iterative optimization on the model based on a projection gradient descent method framework and in combination with a saliency guidance strategy to update disturbance, terminating the optimization process to obtain updated disturbance when an iteration condition is reached, and carrying out the optimization of the target time sequence classification model. And outputting a final confrontation sample based on the obtained disturbance, and completing the generation of the confrontation sample of the time sequence. The attack success rate and the non-concealment of the time sequence confrontation sample are considered at the same time.

The invention discloses an underwater salient target detection method and system based on a double-flow fusion network, and belongs to the technical field of computer vision. The method comprises the following steps: respectively extracting multi-scale features of an RGB image and a depth image through a double-flow encoder; in the shallow layer, fusing and enhancing the edge and detail information of the bimodal features through an edge fusion module; in a deep layer, content-adaptive cross-modal semantic fusion is realized in a frequency domain through a dynamic filtering module; fusing the multi-scale features through a cross-layer aggregation decoder to generate a rough saliency map; extracting detail features from the original RGB image through a global detail purification network; and finally, fusing the rough saliency map and the detail features, and outputting an underwater saliency target prediction map. The objective of the invention is to improve the precision and boundary definition of salient target detection in an underwater complex scene.

The invention relates to the technical field of gift box package appearance detection, in particular to an efficient flaw detection system for batch production of gift box packages based on computer vision. The system comprises a texture direction analysis module which is used for collecting a surface image of a gift box package, preprocessing the surface image to obtain a surface gray level image, and obtaining direction consistency of each pixel point; the suspected crease area acquisition module is used for acquiring a linear feature saliency map and acquiring a suspected crease area; the crease suspicion index acquisition module is used for acquiring a crease suspicion index of the suspected crease area; the crease anomaly detection module is used for acquiring a comprehensive anomaly index of a suspected crease region according to the crease suspicion index of the suspected crease region and a mean value of direction consistency of all pixel points in the suspected crease region; and judging whether the suspected crease region is a crease flaw region or not according to the comprehensive abnormal index. According to the invention, the accuracy of gift box package crease detection can be improved.