1311 results about "Region detection" patented technology

The invention discloses a flight path planning method and system for multi-unmanned aerial vehicle cooperative routing inspection of a distribution network line, and relates to the technical field of unmanned aerial vehicle flight path planning, and the method comprises the steps: carrying out the point cloud data collection based on an unmanned aerial vehicle, and carrying out the data denoising sampling; adaptive clustering is carried out according to the reflection intensity of the sampling data, position codes are introduced to describe point coordinate information, point cloud vectors are constructed, and label classification is carried out on point clouds; and fitting the quadratic curve equation based on the power line label point cloud to obtain a power line model. According to the method, through combination of an adaptive clustering method and a mean shift algorithm, interval division of point cloud reflection intensity is more adaptive, initialization of a clustering center is ensured to be based on data distribution characteristics, through combination of RANSAC fitting, power line point cloud classification and cross region detection, modeling of the power line point cloud is more accurate, and the accuracy of power line point cloud modeling is improved. Meanwhile, tracks of different power lines are clear and separable, and misjudgment of line intersection areas is reduced.

The invention discloses a power equipment detection method and device based on data acquisition of an inspection unmanned aerial vehicle, electronic equipment and a storage medium, and the method comprises the steps: obtaining a multi-view image of to-be-detected power equipment, and inputting the multi-view image into an abnormal region detection model, and generating an anomaly existence probability and a suspected anomaly region coordinate of the equipment under the current view angle. When it is detected that the abnormal probability at a certain visual angle exceeds a preset threshold value, a reinspection instruction is sent to the inspection unmanned aerial vehicle in an adjacent area, and image data of the equipment at a new visual angle are fed back. A second anomaly existence probability is calculated by re-inputting the new view angle image to the anomaly detection model. If the probability exceeds a preset threshold value, judging that the power equipment is abnormal, and sending an alarm signal; otherwise, judging that the equipment is normal. According to the invention, the abnormal accurate positioning and intelligent alarm of the power equipment can be realized, and the efficiency and accuracy of equipment inspection can be effectively improved.

The invention discloses a pump shell welding seam quality detection method based on image segmentation. The method comprises the following steps: generating a steady-state pump shell welding seam image flow under the driving of motion compensation; obtaining a domain adaptive DINOv2 visual embedded feature map; performing adaptive pyramid fusion and cross-scale attention operation on the domain adaptive DINOv2 visual embedded feature map to generate a semantic form segmentation map; generating a semantic-texture fusion mask; performing uncertainty weighted optimization on the semantic-texture fusion mask in combination with the pixel-level confidence map to obtain a weld defect instance map; generating an interpretable texture anomaly heat map; and through multi-view supplementary shooting or manual auditing, supplementary pump shell welding seam image data is obtained, and the steady-state pump shell welding seam image flow is updated. According to the method, the system can continuously keep accurate positioning of the pixel-level segmentation boundary in a weak-label or even non-label migration scene, and boundary drift and area missing detection of a segmentation result are effectively avoided.

The invention discloses a fishing boat name identification method and system based on image processing and deep learning. The method comprises the following steps: S1, fishing boat image acquisition; s2, image preprocessing and enhancement are carried out to realize image noise reduction, deblurring and brightness adjustment processing, and image quality judgment is carried out after processing to form closed-loop optimization; s3, self-adaptive image enhancement is carried out to realize illumination compensation and image dynamic enhancement; s4, ship name area detection and segmentation; s5, ship name region feature extraction and optimization; s6, feedback and optimization of an identification result; s7, character recognition and post-processing; s8, performing correction and verification; s9, log identification and report generation; and S10, displaying and storing a result. According to the invention, the fishing boat name identification precision can be effectively improved.

The invention relates to the technical field of data processing, and provides a target detection method and device, a model training method and device, electronic equipment and a medium. The target detection method comprises the steps that a to-be-recognized image and a query text are acquired, and the query text is used for querying a target object corresponding to the query text in the to-be-recognized image; performing image recognition on the to-be-recognized image to obtain image description features and region detection visual features; performing regional multi-modal fusion processing on the image description features and the regional detection visual features to obtain regional multi-modal fusion features; performing feature fusion processing on text features obtained based on the query text and the regional multi-modal fusion features to obtain text regional fusion features corresponding to the query text; and a target detection result is obtained based on the text features and the text region fusion features, so that the fusion degree of text semantics and image region features is improved, and the accuracy and robustness of target detection in a complex scene are improved.

The invention relates to the technical field of image processing, and provides an image artifact detection method and system based on a deep learning technology, and the method comprises the following steps: carrying out the preprocessing of an obtained to-be-processed image, and carrying out the feature extraction of the preprocessed image; position coding is carried out on the extracted feature map A, and the extracted feature map A is fused with the feature map A; performing multi-layer encoding and decoding on the fused feature map through an encoder and a decoder to obtain a feature map B containing an artifact target area; and generating an anchor frame of an artifact region for a target region in the feature map B, and dynamically adjusting the size and position of the anchor frame according to local information of the down-sampling rate, texture complexity and edge strength of the feature map B to obtain an artifact region detection result after the anchor frame is labeled. Automatic artifact detection and quantitative evaluation of the influence degree can be achieved, the accuracy and efficiency of medical image interpretation are improved, and more reliable data support is provided for clinical decision making.

The invention discloses an archive management system based on artificial intelligence, and the system comprises a data preprocessing module which is used for obtaining and preprocessing original archive data, a missing region detection module which is used for carrying out the missing region detection and feature extraction of the preprocessed archive data, and generating missing region description information; the global search optimization module is used for carrying out global search by adopting a variable-scale Levy flight algorithm based on the missing region description information; the local optimization module is used for carrying out detail optimization on a local missing region by utilizing an improved grey wolf optimization algorithm based on the global search candidate data; the data fusion and consistency matching module is used for fusing the locally optimized filling data and the preprocessed archive data; and the filling quality evaluation and optimization module is used for performing error feedback evaluation on the filled archive data and finally filing the corrected archive data. According to the invention, the overall consistency and rationality of archive filling in the archive management process are improved.

The invention discloses a highway pavement microcrack detection system based on an improved swarm algorithm, and the system comprises an image collection module which is used for outputting a standardized highway pavement image; the image preprocessing module is used for generating a preprocessed highway pavement image; the parameter optimization module is used for performing global search and local refinement search on the preprocessed highway pavement image based on a two-stage bitter fish optimization algorithm model; the image segmentation module is used for generating a preliminary micro-crack candidate region map; the feature extraction module is used for forming a depth feature map; the region reconstruction module is used for outputting a final highway pavement micro-crack region segmentation result; and the detection and output module is used for carrying out candidate region detection and positioning on the highway pavement micro-crack region segmentation result and outputting a final highway pavement micro-crack detection result containing the micro-crack position and region information. According to the method, the response capability to a weak edge is improved, a crack topological structure consistency function is fused in a local refining stage, and the detection structure reduction precision of a micro-crack region is improved.

The invention relates to the field of image cultural relic disease recognition and evaluation, in particular to a cultural relic disease recognition and evaluation system and method based on image analysis, and the system comprises a multispectral image collection module, an image processing module, a disease recognition module, a repair evaluation module, a decision optimization module and a disease database. The multispectral image acquisition module optimizes a light source through a reflective area detection algorithm to obtain a multispectral image; the image processing module uses a multi-band collaborative filtering algorithm to eliminate noise and extract fusion features, and divides disease areas; the disease identification module determines disease positions through a connected region marking algorithm, and performs disease classification by means of a neural symbol model; the repair evaluation module outputs a quantitative score; the decision optimization module weighs the repair effect, the cost and the time by introducing a multi-objective optimization model of a genetic algorithm, and outputs an optimal scheme; according to the invention, the disease identification precision and the automation and scientificity of repair evaluation are improved.

The invention discloses an unstructured file identification method based on primitive identification, and the method comprises the following steps: (1) preprocessing: cutting and zooming an electrical wiring drawing to a required size, and carrying out the gray processing; (2) primitive recognition: constructing a feature extraction network of an electrical primitive detection model by adopting a YOLO electrical primitive detection algorithm fused with an attention mechanism; (3) character recognition, which comprises the following steps: removing primitives, detecting an annotated text area, constructing a text candidate box, segmenting and adjusting an annotated text boundary, recognizing annotated text content, and filtering an electrical text recognition result; and (4) electrical element association relationship analysis, which comprises the following steps: frame element extraction, primitive group region division, template matching and electrical primitive-annotation text association. According to the method, the primitives and characters of the electrical wiring diagram can be obtained, the problem that different fonts and styles appear in marked characters can be solved, and semantic information can be understood.

The invention discloses an ancient textile image restoration system based on artificial intelligence. The system comprises a multi-source image acquisition module, a damaged area detection module, a pattern generation module, a color restoration module, a texture synthesis module and a multi-scale fusion module. The system introduces a wavelet guidance-frequency domain attention mechanism and a rotation invariant Haar wavelet basis function to realize accurate identification and classification of a damaged area; a saliency-guided wavelet decomposition control and self-adaptive threshold denoising method is combined, so that the perception capability of slant textures and edge details is improved; the texture synthesis module constructs a hierarchical modeling strategy fusing Gram style loss, Wasserstein style loss and a total variation regular term, and realizes generation of high-quality textures with unified styles and smooth edges; the system can be widely applied to cultural relic digital repair and display scenes.

Methods, apparatus, systems, and articles of manufacture are disclosed to categorize image text. An example apparatus includes region detection model training circuitry to identify candidate regions in an input image that include text, and generate bounding boxes around respective ones of the identified candidate regions. The example apparatus also includes mask application circuitry to improve optical character recognition (OCR) by applying a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes, and OCR circuitry to perform OCR on the masked input image to obtain text data within the bounding boxes.

The present invention provides a bridge crack detection method, system and storage medium based on the YOLO algorithm, which relates to the technical field of bridge crack detection. The specific steps include: S1. collecting images of bridge crack areas, converting them into grayscale images and using the YOLO algorithm for detection, dividing the cracks into corresponding grid units, drawing crack boundary boxes and saving images of the boundary boxes; S2. determining the four boundaries of the boundary box according to its center coordinates, width and height, and determining the positional relationship between the cracks and the boundary box; S3. determining the number and size of the cracks according to the positional relationship, and drawing a new crack boundary box; S4. calculating the total density and total area of ​​the cracks in the grid unit, and obtaining the comprehensive coefficient of the cracks; S5. comparing the comprehensive coefficient with a preset threshold value to determine the severity of the cracks. The present invention realizes refined crack area detection, and also significantly improves the detection accuracy and adaptability of cracks.

The invention discloses a multi-modal imaging method and system based on acoustic-thermal coupling, and relates to the field of artificial intelligence, and the method comprises the steps: firstly obtaining thermal radiation data and ultrasonic reflection data of a target equipment region, and obtaining thermal radiation embedded features and ultrasonic embedded features through feature representation learning; performing multi-stage compensation on the thermal radiation embedding characteristic to generate a thermal field compensation reference characteristic of each stage; synchronously carrying out equal-stage multi-stage compensation on the ultrasonic embedded features, coupling the ultrasonic compensation result of the previous stage with the thermal field reference features of the corresponding stage between every two continuous stages of compensation, and combining the ultrasonic embedded features and coupling output in the later stage of compensation; and finally, performing imaging by using the acoustic-thermal coupling characteristics after multi-stage compensation to obtain an acoustic-thermal coupling imaging result fusing the thermal distribution characteristics of the target equipment region and the ultrasonic reflection spectrum characteristics. According to the method, deep fusion of acoustic and thermal information is realized through multi-stage feature compensation and dynamic cross-modal coupling, and the comprehensiveness and accuracy of equipment area detection are improved.

The invention provides a grabbing attitude generation method and system based on multi-scale feature fusion and point-by-point prediction, and the method comprises the steps: collecting a grabbing data set, and obtaining a trained grabbing region detection model; constructing a target recognition network combined with multi-scale point cloud feature fusion; according to the trained capture region detection model, using the preprocessed point cloud data as network input, outputting a high-capture confidence region, and mapping the high-capture confidence region to a reference coordinate system of the input point cloud; and a point cloud bounding box is constructed for the output high-grabbing confidence region, a diversified grabbing posture candidate set is generated, and the grabbing posture with the highest quality is selected from the grabbing posture candidate set to serve as the posture of the robot for grabbing the target by calculating grabbing candidate scores. Through construction of a lightweight target recognition network, multi-scale point cloud feature fusion, an efficient training and verification strategy, point cloud data preprocessing and enhancement, accurate grabbing area recognition and attitude estimation and an optimized grabbing selection strategy, the interaction ability and grabbing precision of the mechanical arm to the environment are improved.

The invention discloses a transformer substation defect identification method and system based on cooperation of large and small models. The method comprises the following steps: S1, acquiring image data and preprocessing the image data; s2, acquiring a scene understanding score, and if the score is higher than a preset scene threshold value, synchronously executing S3 and S4; otherwise, only executing S3; s3, inputting the image data into the small model to obtain a detection result, and if the detection result is higher than a first preset threshold value, directly taking the detection result as a current defect identification result; if the detection result is lower than the second preset threshold value, abandoning the detection result; if the image is between the first preset threshold value and the second preset threshold value, obtaining a region detection result through image cutting and local large model reasoning; s4, inputting the image data into the transformer substation defect detection large model to obtain a global detection result; and S5, obtaining a final defect identification result according to the small model and / or regional and / or global detection result. According to the invention, substation defect identification precision can be improved.

The invention relates to the technical field of camera testing, in particular to a camera triangulation distance measurement detection method. Comprising the following steps: selecting a tested camera, and adjusting the position and angle of the camera; according to the positions of the two targets in the image, setting test areas corresponding to different view field angle pictures, and adopting an area detection mode; angular point detection is carried out on the set area, the angular points and sub-pixel-level angular points are obtained, and checkerboard center angular points are judged and corrected; according to a distortion calibration model, selecting a corresponding function to carry out distortion removal calculation on the angular point to obtain a normalized distortion-removed angular point coordinate, and converting the normalized distortion-removed angular point coordinate into an actual distortion-removed pixel coordinate; calculating the calculation distance of two target angular points of each image, establishing a normalized three-dimensional coordinate, calculating the deviation angle of the left and right angular points by using the three points, and combining the measured parameters to obtain the calculation distance of the two angular points; and calculating a precision error. According to the technical scheme, the accuracy of the internal reference calibration data and the reliability of the distortion model can be effectively verified.

The invention relates to the technical field of intelligent diagnosis, in particular to a pressure vessel health monitoring method based on an intelligent algorithm, which comprises the following steps: acquiring pressure, stress and temperature data in real time by using a sensor array, calculating a track evolution parameter set, identifying a damaged area, detecting a fault event, and predicting a health state and residual service life. And establishing a damage grade classification framework, and obtaining a damage grade classification result. According to the method, through multi-source data real-time acquisition and spatial gradient refining processing, by using a pressure and stress distribution coupling relation, accurate damage positioning is realized, the anomaly detection sensitivity is improved, damage boundary coordinates are marked, the expansion rate of adjacent damage areas is quantified, and phase delay is judged in combination with a multi-period pressure track; fault recognition stability is enhanced, abnormal event classification precision is optimized, a material fatigue accumulation and pressure uniformity correlation means is adopted, health score attenuation observation is enhanced, health state quantification depth is improved, and life prediction reliability is expanded.

The invention discloses a natural resource supervision method based on three-dimensional GIS scene and video fusion, and relates to the technical field of intelligent natural supervision. Comprising the steps of collecting a video stream through a preset observation point, extracting a continuous frame sequence, and calling a space parameter set; performing dynamic region detection on the video frame sequence to generate change feature data; mapping the change characteristic data into a topological structure to be updated based on the projection matrix; calling a pre-constructed three-dimensional GIS scene model, and positioning an update area; according to the type identifier of the change feature data, executing an operation of deleting the associated geometry or constructing and fusing a new geometry; adding a state attribute label to the updated geometry, and storing an update model; the resource change rate is calculated, and when the change rate exceeds a preset industry threshold value, an early warning instruction is generated and pushed to the control terminal. And the space accuracy, the model updating efficiency and the early warning reliability of resource change identification in a complex natural scene are remarkably improved.

The invention discloses a multi-spectral image fusion building surface biological attachment area detection method and device and a medium, relates to the technical field of image processing, and discloses a multi-spectral image fusion building surface biological attachment area detection method comprising the following steps: based on a to-be-detected building, obtaining a visible light image collected by a visible light camera of an unmanned aerial vehicle, the multispectral camera acquires a multispectral image sequence based on at least two different frequency bands; registering the visible light image and the multispectral image based on a calibration and preprocessing module to obtain a pixel-level aligned visible light image and multispectral image sequence; and according to a deep learning feature identification module, identifying the visible light image and the multispectral image sequence after pixel-level alignment, and obtaining a pixel-level segmentation result of the organism attachment area of the to-be-detected building. Therefore, detection is carried out based on the unmanned aerial vehicle, deep learning and an image feature fusion mode are combined, and the building surface bioattachment recognition accuracy is improved.

The invention provides a short-baseline binocular three-dimensional human body posture reconstruction method and system, and the method comprises the steps: collecting original left and right image pairs, and carrying out the detection of a human body region; carrying out human body image pair matching and scale transformation on the detected human body region; parallax features are obtained based on the processed left and right human body area image pairs, feature fusion is carried out on the parallax features and human body features extracted by the three-dimensional human body posture estimation sub-network, a parallax image initial result is obtained based on feature fusion information, a loss function is established with the human body features to carry out parallax optimization, and an optimized parallax image and a scene depth image are obtained; human body features are obtained based on left and right human body area image pairs, feature fusion is carried out on the human body features and parallax features, two-dimensional human body joint points are obtained through two-dimensional posture estimation according to feature fusion results, and a three-dimensional human body posture initial result is obtained based on binocular camera parameters and a triangulation calculation method; and establishing a loss function with the scene depth map and human body prior supervision, and performing three-dimensional human body posture optimization to obtain an optimized three-dimensional human body posture.

The invention belongs to the technical field of medical image processing, and discloses a blood vessel segmentation reconstruction and bleeding analysis method and device. In the bleeding area detection model, detecting whether a bleeding area exists or not; and performing blood vessel segmentation and blood vessel three-dimensional reconstruction operation. According to the method, the time sequence image is adopted for comprehensive analysis, and the problems that blood vessel details are lost due to insufficient diffusion of a contrast agent and the image is blurred due to excessive diffusion are effectively solved. Based on an 8-neighborhood pixel tracking method, accurately identifying blood vessel cross points, and performing segmentation processing on the skeleton line by using the cross points. By constructing the topological structure of the blood vessel, the feature point matching efficiency is greatly improved, and the automatic three-dimensional reconstruction process is also realized. By introducing the architecture design of the hybrid convolutional neural network and the state space model, the local fine-grained feature extraction capability and the global long-range dependence modeling capability are effectively combined, so that the segmentation precision and robustness of the intracranial hemorrhage region are remarkably improved.

The invention provides a large coal target detection method and device, and relates to the technical field of computer vision, and the method comprises the steps: training a target detection model based on a data set obtained through carrying out the image enhancement of a detection region image and a coal block image of a scraper conveyor and carrying out the YOLO-OBB data format labeling, and obtaining a target detection result; generating an obb target detection model for identifying an area detection frame corresponding to the detection area of the scraper conveyor and a coal block detection frame corresponding to the coal block; the method comprises the following steps: acquiring a real-time detection area coal flow video of a scraper conveyor, deducing the real-time detection area coal flow video of the scraper conveyor to obtain an area ratio between an obb area detection frame and an obb coal briquette detection frame corresponding to each frame of image, judging whether the coal briquette belongs to large coal, and judging whether the coal briquette belongs to fixed abnormal coal briquettes based on the number and displacement of optical flow points in the obb coal briquette detection frame of each frame of image. Therefore, based on the obb target detection model, extraction and identification of the obb detection frame of the scraper conveyor and the coal briquette target in the video stream are improved, and accurate detection, classification and identification of the coal briquettes are further realized.

The invention discloses a semantic SLAM method combining instance segmentation and optical flow estimation, and belongs to the technical field of robot positioning and navigation.The method comprises the steps that a dynamic region detection thread, a semantic map building thread and a point-line feature fusion method are fused into an ORB-SLAM 3 system; the dynamic object detection thread is combined with an improved YOLOv8-seg semantic segmentation network and an optical flow estimation network RAFT, images are collected through an RGBD camera, point feature extraction and line feature extraction are carried out on the obtained images, the input images are segmented through the semantic segmentation network, masks of objects are obtained, area masks are obtained through the optical flow estimation network, and the dynamic object detection thread is obtained. And combining semantic segmentation and optical flow estimation masks to determine a final dynamic region, and finally removing point features and line features in the dynamic region. According to the method, the precision and robustness of the SLAM system in a dynamic environment and a weak texture environment are improved by combining semantic information and optical flow information to remove dynamic objects and fusing point and line features, and the method can be applied to real-time semantic map construction in a dynamic scene.

The invention relates to an unmanned aerial vehicle autonomous exploration method for indoor / underground unknown space. The method comprises the following steps: S1, acquiring a pose of an unmanned aerial vehicle and a depth image provided by a depth camera, and constructing an ESDF environment map; s2, performing boundary search in the constructed environment map, and generating high-quality candidate viewpoints by adopting a mixed viewpoint method based on an existing boundary; s3, formulating a more reasonable boundary exploration sequence by adopting a novel boundary priority strategy and an isolated small region detection method; s4, recording a global path of each time by adopting a historical perception strategy, and calculating the motion smoothing cost to obtain an expected global exploration path; and S5, according to path planning, an LKH algorithm is adopted to solve the traversal problem of a global boundary viewpoint, an optimal traversal sequence is obtained, and an A * algorithm is utilized to search a feasible local path from the current position of the unmanned aerial vehicle to a first candidate viewpoint in a map. And S6, adopting a third-order uniform B spline curve, and introducing a penalty function to optimize the trajectory to obtain an expected trajectory. And S7, the expected flight path is sent to a flight controller of the unmanned aerial vehicle, and the unmanned aerial vehicle flies according to the flight path by applying a PD controller.

The invention discloses a focus segmentation method fusing PET and CT bimodal images, and relates to the technical field of computers. The method comprises the following steps: acquiring focus image data, separating the focus image data to obtain CT data and PET data, and inputting the CT data and the PET data into a bimodal medical image segmentation model to obtain a preliminary segmentation result; a difference region is generated based on the preliminary segmentation result, difference region detection is carried out on the difference region, and click signal data is generated; the difference region is a region which is not marked as a focus in the preliminary segmentation result; training the bimodal medical image segmentation model for a preset number of times through the click signal data, the CT data and the PET data to obtain a segmentation result; and determining a segmentation result generated by the last training as a lesion image segmentation result. The method can improve the medical image lesion segmentation precision.

Systems and methods for video coding and decoding using region packing are provided. At an encoder, a region detection module receives a video frame for encoding, identifies regions of interest in the video frame, and generates a bounding box for each region of interest. A region extractor module obtains the pixels within the bounding box from the video frame. A region packing module receives the identified regions of interest and arranges the bounding boxes within a packed frame substantially reducing the data to be encoded outside the identified regions of interest. A video encoder receives the packed frame and generates an encoded bitstream therefrom. At the decoder, the encoded bitstream is decoded and parameters sufficient to place the regions within a reconstructed frame are extracted. A reconstructed frame is generated which substantially maintains the spatial relationship and size of regions of interest in the original video frame.

The invention relates to the technical field of face recognition, and discloses a face recognition method and system suitable for an outdoor electronic instrument to solve the problem of low recognition precision under complex illumination, posture change and shielding conditions in the prior art, and the method comprises the steps: S1, collecting image data, and carrying out the preprocessing; s2, performing face region detection and face key point positioning on the illumination enhanced image; s3, performing affine transformation based on the face detection result set to perform face alignment; s4, extracting a face feature vector based on a ResNet100 structure; s5, performing attitude-guided feature reconstruction processing on the face feature vector to obtain an attitude-enhanced face feature vector; s6, performing feature normalization and identity classification on the posture-enhanced face feature vector, and outputting a face recognition result; and S7, outputting a final identity recognition result and confidence. According to the method, an efficient and scientific optimization scheme can be provided in outdoor face recognition, and remarkable technical values and economic benefits are brought to practical application.

The invention discloses a power line anomaly detection method and system based on an unmanned aerial vehicle. The method comprises the steps that a server analyzes a power line detection request to obtain a detection area and a detection time period; planning an initial detection track, generating a power line detection instruction based on the initial detection track, the detection area and the detection time period, and issuing the instruction to the unmanned aerial vehicle; the unmanned aerial vehicle executes an inspection task in the electronic fence, the initial detection track is dynamically corrected in the inspection process, and a flight time sequence image is collected; analyzing the flight time sequence image through a line anomaly detection model, and outputting a line anomaly detection result; an actual detection track is recorded, and abnormity marking is carried out on the actual detection track; and recording, encrypting and uploading an inspection log to the server. The method has the advantages that the detection efficiency, the intelligent level and the safety management capability of power line anomaly detection are greatly improved.

A region detection device (1) according to the present disclosure includes a line segment detection unit (32) that detects a line segment from a captured image including an image of a target object having a linear band shape, a main line segment extraction unit (33) that extracts a main line segment (SL) that is a line segment having a length equal to or more than a predetermined value; a correction unit (34) that generates, as a corrected image, a rotated image obtained by rotating the captured image such that the main line segment (SL) is perpendicular or parallel to an arrangement direction of pixels in the captured image, and a region detection unit (35) that detects a target object region indicating the image of the target object from the corrected image.