WO2021153646A1 - Procédé de génération de données, dispositif de génération de données, procédé de détection d'anomalie, dispositif de détection d'anomalie, et programme - Google Patents

Procédé de génération de données, dispositif de génération de données, procédé de détection d'anomalie, dispositif de détection d'anomalie, et programme Download PDF

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WO2021153646A1
WO2021153646A1 PCT/JP2021/002956 JP2021002956W WO2021153646A1 WO 2021153646 A1 WO2021153646 A1 WO 2021153646A1 JP 2021002956 W JP2021002956 W JP 2021002956W WO 2021153646 A1 WO2021153646 A1 WO 2021153646A1
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area
target data
detection target
anomaly
label
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English (en)
Japanese (ja)
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真樹 齋藤
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株式会社Ｐｒｅｆｅｒｒｅｄ Ｎｅｔｗｏｒｋｓ
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Definitions

  • This disclosure relates to a model generation method, a model generation device, an abnormality detection method, an abnormality detection device, and a program.
  • the anomaly detection problem that identifies whether a given data contains anomalies by machine learning can be widely applied in industrial applications. Examples include visual inspection, medical image diagnosis, abnormality detection of surveillance cameras, remote sensing, and automatic driving.
  • the object of the present disclosure is to provide a technique for detecting an abnormality in the data to be detected with high accuracy.
  • one aspect of the present disclosure is A step in which one or more processors input detection target data into an abnormality detection model and acquire an abnormality degree map from the abnormality detection model.
  • a step in which the one or more processors obtain a loss value of the detection target data from the abnormality score and the label value.
  • a step in which the one or more processors update the parameters of the anomaly detection model based on the loss value.
  • the present invention relates to a model generation method having.
  • the model generator 100 is trained according to the training process described in detail below using the training labeled sample data stored in the database 300. You may train the anomaly detection model of the target.
  • the anomaly detection model is realized by, for example, a convolutional neural network, receives sample data such as image data as an input, and outputs an anomaly map (anomaly map) indicating an abnormality location in the image data.
  • the model generation device 100 may provide the trained abnormality detection model to the abnormality detection device 200.
  • the abnormality detection device 200 inputs the sample data to be detected into the abnormality detection model, and whether the sample data to be detected is normal or abnormal based on the abnormality degree score derived from the abnormality degree map output from the abnormality detection model. You may judge.
  • a model generator 100 according to an embodiment of the present disclosure will be described with reference to FIG.
  • FIG. 2 is a block diagram showing a functional configuration of the model generation device 100 according to the embodiment of the present disclosure.
  • the model generation device 100 may include a model processing unit 110, a label conversion unit 120, and a loss calculation unit 130.
  • the model processing unit 110 may input the detection target data into the abnormality detection model and acquire the abnormality degree map from the abnormality detection model. Specifically, the model processing unit 110 may input sample data such as image data among the sample data with a training label stored in the database 300 into the abnormality detection model to acquire an abnormality degree map.
  • the abnormality degree map may indicate an abnormality portion in the sample data, and may be, for example, a two-dimensional map in which a portion having a high degree of abnormality is indicated by a large value and a portion other than the portion is indicated by a small value.
  • the anomaly detection model may be a convolutional neural network, or it may detect anomalies based on the difference between normal sample data without anomalies and input detection target data. Or it may be an appropriate calculation model.
  • the model processing unit 110 may update the parameters of the convolutional neural network of the anomaly detection model or the calculation model according to the training method described later.
  • the label conversion unit 120 may convert the label of the detection target data into an area label.
  • the area label may include a pair of area data indicating an area in the detection target data and a label value indicating whether the area is normal or abnormal.
  • the label conversion unit 120 converts the labels L1, L2 or L3 corresponding to the sample data input to the abnormality detection model among the training labeled sample data into the area labels RL1, RL2 or RL3, respectively. May be good.
  • the area label may be composed of a pair of a binary image indicating an area in the sample data and a binary value "good” or "bad” indicating whether the area is normal or abnormal, and the label conversion unit 120 may be composed of a pair.
  • Area labels RL1, RL2 or RL3 may be generated corresponding to the type of label L1, L2 or L3 to be input.
  • the label L1 of the detection target data may include a binary value (good / bad) indicating whether or not the entire detection target data contains an abnormality. For example, if the sample data contains an abnormality such as a scratch, the label of the sample data may be set to "bad", and if the sample data does not contain an abnormality and is normal, the sample is concerned. The label of the data may be set to "good”.
  • the area label RL1 corresponding to the detection target data may include a pair of the area data indicating the entire detection target data and the label value indicating the binary value.
  • the label conversion unit 120 uses the label L1 in which the entire sample data is set to “bad”, the area data indicating the entire sample data, and the label value “indicating that the area is abnormal”. It may be converted to the area label RL1 indicating the pair with “bad”.
  • the label of the detection target data may include rectangular information indicating that the rectangular area in the detection target data contains an abnormality.
  • the label L2 of the sample data may indicate this rectangular region.
  • a rectangular area may be set for each abnormal part, or one or more rectangular areas including all or a part of the abnormal parts are set. You may.
  • the area label RL3 corresponding to the detection target data may include a pair of the area data indicating the rectangular area and the label value “bad” indicating that the area is abnormal.
  • the area label RL2 corresponding to the detection target data is a pair of the area data indicating the area excluding the rectangular area in the detection target data and the label value “good” indicating that the area excluding the rectangular area is normal. And may be further included.
  • the label conversion unit 120 uses the label L2 formed by the rectangular area set in “bad” as the area data indicating the area indicated by the diagonal line and the label indicating that the area is normal.
  • the label L3 of the detection target data may include area information indicating that the designated area in the detection target data contains an abnormality. For example, when the designated area in the sample data contains at least one abnormality such as a scratch, the label L3 of the sample data may indicate this designated area. When the sample data contains a plurality of abnormalities, a designated area may be set for each abnormal part, or one or more designated areas including all or some abnormal parts may be set. May be done. In this case, the area label RL5 corresponding to the detection target data may include a pair of the area data indicating the designated area in the detection target data and the label value “bad” indicating that the designated area is abnormal. good.
  • the area label RL4 corresponding to the detection target data is a pair of the area data indicating the area excluding the designated area in the detection target data and the label value “good” indicating that the area excluding the designated area is normal. And may be further included.
  • the label conversion unit 120 displays the label L3 by the designated area set in "bad” with the area data indicating the area indicated by the diagonal line and the label indicating that the area is normal.
  • the loss calculation unit 130 may acquire the abnormality degree score from the abnormality degree map and the area data of the area label. Specifically, the loss calculation unit 130 determines the region based on the anomaly degree map of the sample data and the binary image showing the region in the sample data (for example, the entire sample data, the binary image of the rectangular region or the designated region). A focused area anomaly map may be generated, or an anomaly score may be obtained from the area anomaly map by a pooling function.
  • the loss calculation unit 130 collates the binary image of each pair of area labels with the anomaly degree map, and generates a partial anomaly degree map focusing only on the area shown in the binary image as an area anomaly degree map. Then, the loss calculation unit 130 may apply the pooling function to the generated area abnormality degree map to calculate the abnormality degree score representing the abnormality degree of the region.
  • a global polling function such as global max polling, global average polling, and global Lp polling may be used.
  • the loss calculation unit 130 may acquire the loss value of the detection target data from the abnormality degree score for the area abnormality degree map and the label value of the area.
  • any loss function generally used in deep learning may be used for the calculation of the loss value, and for example, sigmoid cross entropy, contrast loss, triple loss, L2 softmax loss, etc. can be used.
  • the loss calculation unit 130 may calculate the abnormality degree score for all the area labels according to the above-mentioned procedure, or may calculate the loss value of each area label from the calculated abnormality degree score. Then, the loss calculation unit 130 may calculate the sum of the calculated loss values for the area label corresponding to each sample data, and the model processing unit uses the total of the calculated loss values as the loss value of the sample data. You may call me 110.
  • the model processing unit 110 may update the parameters of the abnormality detection model based on the loss value of each detection target data. For example, when the anomaly detection model is realized by a neural network, when the loss value of each sample data is acquired from the loss calculation unit 130, the model processing unit 110 optimizes any appropriate parameter such as the stochastic gradient descent method. According to the method, the parameters of the abnormality detection model may be updated based on the acquired loss value. Specifically, the model processing unit 110 may update the parameter for the loss value corresponding to the label value “good” so as to reduce the loss value, and the model processing unit 110 may update the parameter corresponding to the label value “bad”. The parameter may be updated so as to increase the loss value.
  • the model processing unit 110 may continue the update process until the end condition is satisfied, and provides the abnormality detection device 200 as a trained abnormality detection model with the abnormality detection model finally acquired after the end condition is satisfied. You may.
  • the termination condition for example, the above-mentioned procedure may be executed for a predetermined number of sample data.
  • the model generation process may be realized by the model generation device 100 described above, and is realized, for example, by executing a program or instruction stored in a memory by one or more processors or processing circuits of the model generation device 100. May be done.
  • FIG. 3 is a flowchart showing a model generation process according to an embodiment of the present disclosure.
  • the model generation device 100 may input the detection target data into the abnormality detection model, or may acquire the abnormality degree map from the abnormality detection model.
  • the model generation device 100 may input the acquired sample data into the abnormality detection model to be trained, and acquires an abnormality degree map from the abnormality detection model. May be good.
  • the anomaly degree map may be a two-dimensional map showing the position of an abnormal part in the sample data.
  • the real number indicating whether or not the sample data contains scratches and the said. It may consist of a pair with an image showing the position of the scratch.
  • the image data for the visual inspection may be a monochrome image or a color image.
  • the detection target data may be any appropriate data according to the detection target, such as hyperspectral image data, depth image data, audio data, waveform data, and three-dimensional data.
  • the model generation device 100 may convert the label of the detection target data into the area label.
  • the detection target data is image data for visual inspection
  • a label indicating the presence or absence of scratches in the image data is stored in the database 300 as training data in association with the image data.
  • the model generation device 100 may acquire a label corresponding to the image data input to the abnormality detection model in step S101 from the database 300, or may convert the label into area data as an intermediate representation.
  • the area data may include a pair of a binary image indicating an area in the image data and a binary value indicating whether the area is normal or abnormal, and is generated according to the label type. May be done. In the binary image, a large value may be assigned to an area with a high degree of anomaly, and a small value may be assigned to an area other than the normal area.
  • the model generator 100 has the model generator 100 as shown in RL1.
  • a region label for the image data may be generated to include a pair of a binary image indicating the entire image data and a label value "bad" indicating the binary value.
  • the model generator 100 indicates in RL2.
  • An area label for the image data may be generated to include a pair of the image and a label value "good” indicating that the area other than the rectangular area is normal.
  • the model generator 100 indicates in RL4.
  • An area label for the image data may be generated so as to include a pair of an image and a label value "good” indicating that the area other than the designated area is normal.
  • the model generation device 100 may acquire the anomaly degree score from the anomaly degree map and the area data. For example, when the anomaly degree map and the area label RL1 as shown in FIG. 2 are acquired for the sample data, the model generation device 100 creates the anomaly degree map and the binary image as the area data showing the entire image data.
  • global max polling may be applied, and for example, a region score ⁇ 1.28, bad ⁇ composed of an abnormality score “1.28” and a label value “bad” may be acquired.
  • the model generator 100 may collate the anomaly degree map with the binary image of the RL2. , A partial anomaly degree map focusing only on the area of the label value “good” in the binary image may be generated as an area anomaly degree map. Then, the model generator 100 applies global max polling to the region abnormality degree map, and for example, the region score ⁇ 0.30, which is composed of the abnormality degree score “0.30” and the label value “good”. Get good ⁇ .
  • the model generation device 100 collates the abnormality degree map with the binary image of RL3, and generates a partial abnormality degree map focusing only on the area of the label value “bad” in the binary image as the area abnormality degree map. May be good. Then, the model generator 100 applies global max polling to the region abnormality degree map, and for example, the region score ⁇ 1.26, which is composed of the abnormality degree score “1.26” and the label value “bad”. Get bad ⁇ .
  • the model generator 100 may collate the anomaly degree map with the binary image of the RL4. , A partial anomaly degree map focusing only on the area of the label value “good” in the binary image may be generated as an area anomaly degree map. Then, the model generator 100 applies global max polling to the region abnormality degree map, and for example, the region score ⁇ 0.30, which is composed of the abnormality degree score “0.30” and the label value “good”. Get good ⁇ .
  • the model generation device 100 collates the abnormality degree map with the binary image of the RL5, and generates a partial abnormality degree map focusing only on the area of the label value “bad” in the binary image as the area abnormality degree map. Then, the model generator 100 applies global max polling to the region abnormality degree map, and for example, the region score ⁇ 1.24, which is composed of the abnormality degree score “1.24” and the label value “bad”. Get bad ⁇ .
  • the model generation device 100 may acquire the loss value of the detection target data from the abnormality degree score and the label value. For example, when the region score RS1 is acquired for the sample data, the model generator 100 applies the anomaly score to any appropriate loss function such as sigmoid cross entropy, cross entropy, triple loss, L2 softmax loss, and the like. The loss value of the sample data may be calculated. Further, when the region scores RS2 and RS3 are acquired for the sample data, the model generator 100 applies each abnormality degree score of RS2 and RS3 to the loss function, and the sum of the derived loss values is the loss of the sample data. It may be determined as a value.
  • the model generator 100 applies each abnormality score of RS4 and RS5 to the loss function, and the sum of the derived loss values is the sum of the derived loss values of the sample data. It may be determined as a loss value.
  • the model generation device 100 may update the parameters of the abnormality detection model based on the loss value of the detection target data.
  • the model generator 100 follows an appropriate parameter optimization method such as a stochastic gradient descent method, and the anomaly detection model is based on the acquired loss value. You may update the parameters of.
  • the model generator 100 may update the parameter for the loss value corresponding to the label value “good” so as to reduce the loss value, and for the loss value corresponding to the label value “bad”, the parameter may be updated. , The parameters may be updated to increase the loss value.
  • the model generator 100 may determine whether the end condition is satisfied.
  • the end condition may be that steps S101 to S105 are executed for a predetermined number of sample data.
  • the model generation process is completed, and the finally acquired abnormality detection model may be provided to the abnormality detection device 200.
  • the model generator 100 may repeat steps S101 to S105 described above for the next sample data.
  • the abnormality detection device 200 may determine the presence or absence of an abnormality in unknown sample data by using the abnormality detection model generated by the model generation device 100.
  • FIG. 4 is a block diagram showing a functional configuration of the abnormality detection device 200 according to the embodiment of the present disclosure.
  • the abnormality detection device 200 may have an abnormality degree map generation unit 210 and an abnormality detection unit 220.
  • the abnormality degree map generation unit 210 may input the detection target data into the trained abnormality detection model and acquire the abnormality degree map from the trained abnormality detection model. Specifically, when the abnormality degree map generation unit 210 receives sample data such as unknown image data to be detected, the received sample data is input to the trained abnormality detection model and output from the trained abnormality detection model. The abnormality degree map may be sent to the abnormality detection unit 220. For example, in the visual inspection, a plurality of image data acquired by imaging the article to be inspected from various angles can be input to the abnormality detection device 200. In this case, the anomaly degree map generation unit 210 may generate an anomaly degree map for each type of image data by using a trained anomaly detection model corresponding to these a plurality of types of image data.
  • the abnormality detection unit 220 may acquire the abnormality degree score from the abnormality degree map according to the pooling function, or may determine whether the detection target data is normal or abnormal based on the abnormality degree score. Specifically, when the anomaly map is acquired from the anomaly map generation unit 210, the anomaly detection unit 220 applies any appropriate pooling function such as global max polling to the anomaly map to determine the anomaly score. You may. Then, the abnormality detection unit 220 may determine whether or not there is an abnormality in the sample data to be detected by determining whether or not the determined abnormality degree score is equal to or higher than a predetermined threshold value.
  • the abnormality detection unit 220 may determine that there is an abnormality in the sample data when the abnormality degree score is equal to or more than a predetermined threshold value, and when the abnormality degree score is less than a predetermined threshold value, the sample may be determined.
  • the data may be determined to be normal.
  • An abnormality detection process according to an embodiment of the present disclosure will be described with reference to FIG.
  • the anomaly detection process is realized by the above-mentioned anomaly detection device 200, for example, by executing a program or instruction in which one or more processors or processing circuits of the anomaly detection device 200 are stored in one or more memories. It may be realized.
  • FIG. 5 is a flowchart showing an abnormality detection process according to an embodiment of the present disclosure.
  • the abnormality detection device 200 may input the detection target data into the trained abnormality detection model and acquire the abnormality degree map from the trained abnormality detection model.
  • the abnormality detection device 200 may acquire the abnormality degree score from the abnormality degree map according to the pooling function.
  • the abnormality detection device 200 may determine whether the abnormality degree score is equal to or higher than a predetermined threshold value. When the abnormality degree score is equal to or higher than a predetermined threshold value (S203: YES), the abnormality detection device 200 may determine that the sample data is abnormal in step S204. On the other hand, when the abnormality degree score is less than a predetermined threshold value (S203: NO), the abnormality detection device 200 may determine that the sample data is normal in step S205.
  • a predetermined threshold value S203: YES
  • the abnormality detection device 200 may determine that the sample data is abnormal in step S204.
  • the abnormality degree score is less than a predetermined threshold value (S203: NO)
  • the abnormality detection device 200 may determine that the sample data is normal in step S205.
  • a part or all of each device (model generation device 100 or abnormality detection device 200) in the above-described embodiment may be composed of hardware, a CPU (Central Processing Unit), or a GPU (Graphics Processing Unit). ) Etc.
  • the software may be composed of information processing of software (program) executed.
  • the software that realizes at least a part of the functions of each device in the above-described embodiment is a flexible disk, a CD-ROM (Compact Disc-Read Only Memory), or a USB (Universal).
  • Serial Bus Information processing of software may be executed by storing it in a non-temporary storage medium (non-temporary computer-readable medium) such as a memory and reading it into a computer.
  • the software may be downloaded via a communication network.
  • information processing may be executed by hardware by mounting the software on a circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).
  • the type of storage medium that stores the software is not limited.
  • the storage medium is not limited to a removable one such as a magnetic disk or an optical disk, and may be a fixed storage medium such as a hard disk or a memory. Further, the storage medium may be provided inside the computer or may be provided outside the computer.
  • FIG. 6 is a block diagram showing an example of the hardware configuration of each device (model generation device 100 or abnormality detection device 200) in the above-described embodiment.
  • each device includes a processor 101, a main storage device 102 (memory), an auxiliary storage device 103 (memory), a network interface 104, and a device interface 105, which are connected via a bus 106. It may be realized as a computer 107.
  • the computer 107 of FIG. 6 includes one component for each component, but may include a plurality of the same components. Further, although one computer 107 is shown in FIG. 6, software is installed on a plurality of computers, and each of the plurality of computers executes the same or different part of the software. May be good. In this case, it may be a form of distributed computing in which each computer communicates via a network interface 104 or the like to execute processing. That is, each device (model generation device 100 or abnormality detection device 200) in the above-described embodiment realizes a function by executing an instruction stored in one or a plurality of storage devices by one or a plurality of computers. It may be configured as a system. Further, the information transmitted from the terminal may be processed by one or a plurality of computers provided on the cloud, and the processing result may be transmitted to the terminal.
  • each device model generation device 100 or abnormality detection device 200 in the above-described embodiment can be performed in parallel using one or more processors or by using a plurality of computers via a network. It may be executed. Further, various operations may be distributed to a plurality of arithmetic cores in the processor and executed in parallel processing. In addition, some or all of the processes, means, etc. of the present disclosure may be executed by at least one of a processor and a storage device provided on the cloud capable of communicating with the computer 107 via a network. As described above, each device in the above-described embodiment may be in the form of parallel computing by one or a plurality of computers.
  • the processor 101 may be an electronic circuit (processing circuit, Processing circuit, Processing circuitry, CPU, GPU, FPGA, ASIC, etc.) including a computer control device and an arithmetic unit. Further, the processor 101 may be a semiconductor device or the like including a dedicated processing circuit. The processor 101 is not limited to an electronic circuit using an electronic logic element, and may be realized by an optical circuit using an optical logic element. Further, the processor 101 may include a calculation function based on quantum computing.
  • the processor 101 can perform arithmetic processing based on data and software (programs) input from each device or the like having an internal configuration of the computer 107, and output the arithmetic result or control signal to each device or the like.
  • the processor 101 may control each component constituting the computer 107 by executing an OS (Operating System) of the computer 107, an application, or the like.
  • OS Operating System
  • Each device (model generation device 100 or abnormality detection device 200) in the above-described embodiment may be realized by one or a plurality of processors 101.
  • the processor 101 may refer to one or more electronic circuits arranged on one chip, or may refer to one or more electronic circuits arranged on two or more chips or two or more devices. You may point. When a plurality of electronic circuits are used, each electronic circuit may communicate by wire or wirelessly.
  • the main storage device 102 is a storage device that stores instructions executed by the processor 101, various data, and the like, and the information stored in the main storage device 102 is read out by the processor 101.
  • the auxiliary storage device 103 is a storage device other than the main storage device 102. Note that these storage devices mean any electronic component capable of storing electronic information, and may be a semiconductor memory.
  • the semiconductor memory may be either a volatile memory or a non-volatile memory.
  • the storage device for storing various data in each device (model generation device 100 or abnormality detection device 200) in the above-described embodiment may be realized by the main storage device 102 or the auxiliary storage device 103, and may be realized by the processor 101. It may be realized by the built-in internal memory.
  • the storage unit in the above-described embodiment may be realized by the main storage device 102 or the auxiliary storage device 103.
  • processors may be connected (combined) to one storage device (memory), or a single processor may be connected.
  • a plurality of storage devices (memory) may be connected (combined) to one processor.
  • Each device (model generation device 100 or abnormality detection device 200) in the above-described embodiment is a plurality of processors connected (combined) to at least one storage device (memory) and the at least one storage device (memory).
  • it may include a configuration in which at least one of the plurality of processors is connected (combined) to at least one storage device (memory).
  • this configuration may be realized by a storage device (memory) and a processor included in a plurality of computers.
  • a configuration in which the storage device (memory) is integrated with the processor for example, a cache memory including an L1 cache and an L2 cache may be included.
  • the network interface 104 is an interface for connecting to the communication network 108 wirelessly or by wire. As the network interface 104, an appropriate interface such as one conforming to an existing communication standard may be used. The network interface 104 may exchange information with the external device 109A connected via the communication network 108.
  • the communication network 108 may be any one of WAN (Wide Area Network), LAN (Local Area Network), PAN (Personal Area Network), or a combination thereof, and may be a combination of the computer 107 and the external device 109A. Any information can be exchanged between them.
  • WAN Wide Area Network
  • LAN Local Area Network
  • PAN Personal Area Network
  • An example of WAN is the Internet
  • an example of LAN is IEEE802.11, Ethernet (registered trademark), etc.
  • PAN is Bluetooth (registered trademark), NFC (Near Field Communication), etc.
  • the device interface 105 is an interface such as USB that directly connects to the external device 109B.
  • the external device 109A is a device connected to the computer 107 via a network.
  • the external device 109B is a device that is directly connected to the computer 107.
  • the external device 109A or the external device 109B may be an input device as an example.
  • the input device is, for example, a device such as a camera, a microphone, a motion capture, various sensors, a keyboard, a mouse, or a touch panel, and gives the acquired information to the computer 107. Further, it may be a device including an input unit, a memory and a processor such as a personal computer, a tablet terminal, or a smartphone.
  • the external device 109A or the external device 109B may be an output device as an example.
  • the output device may be, for example, a display device such as an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube), a PDP (Plasma Display Panel), or an organic EL (Electro Luminescence) panel, and outputs audio or the like. It may be a speaker or the like. Further, it may be a device including an output unit such as a personal computer, a tablet terminal, or a smartphone, a memory, and a processor.
  • the external device 109A or the external device 109B may be a storage device (memory).
  • the external device 109A may be a network storage or the like, and the external device 109B may be a storage such as an HDD.
  • the external device 109A or the external device 109B may be a device having some functions of the components of each device (model generation device 100 or abnormality detection device 200) in the above-described embodiment. That is, the computer 107 may transmit or receive a part or all of the processing result of the external device 109A or the external device 109B.
  • the expression (including similar expressions) of "at least one (one) of a, b and c" or "at least one (one) of a, b or c" is used. When used, it includes any of a, b, c, ab, ac, bc, or abc. It may also include multiple instances of any element, such as a-a, a-b-b, a-a-b-b-c-c, and the like. It also includes adding elements other than the listed elements (a, b and c), such as having d, such as a-b-c-d.
  • connection when the terms "connected” and “coupled” are used, direct connection / coupling and indirect connection / coupling are used. , Electrically connected / combined, communicatively connected / combined, operatively connected / combined, physically connected / combined, etc. Intended as a term.
  • the term should be interpreted as appropriate according to the context in which the term is used, but any connection / combination form that is not intentionally or naturally excluded is not included in the term. It should be interpreted in a limited way.
  • the physical structure of the element A can perform the operation B. Including that the element A has a configuration and the permanent or temporary setting (setting / configuration) of the element A is set (configured / set) to actually execute the operation B. good.
  • the element A is a general-purpose processor
  • the processor has a hardware configuration capable of executing the operation B, and the operation B is set by setting a permanent or temporary program (instruction). It suffices if it is configured to actually execute.
  • the element A is a dedicated processor, a dedicated arithmetic circuit, or the like, the circuit structure of the processor actually executes the operation B regardless of whether or not the control instruction and data are actually attached. It only needs to be implemented.
  • finding a global optimal value finding an approximation of a global optimal value, finding a local optimal value, and local optimization It should be interpreted as appropriate according to the context in which the term was used, including finding an approximation of the value. It also includes probabilistically or heuristically finding approximate values of these optimal values.
  • the respective hardware when a plurality of hardware performs a predetermined process, the respective hardware may cooperate to perform the predetermined process, or some hardware may perform the predetermined process. You may do all of the above. Further, some hardware may perform a part of a predetermined process, and another hardware may perform the rest of the predetermined process.
  • the hardware that performs the first process and the hardware that performs the second process when expressions such as "one or more hardware performs the first process and the one or more hardware performs the second process" are used. , The hardware that performs the first process and the hardware that performs the second process may be the same or different. That is, the hardware that performs the first process and the hardware that performs the second process may be included in the one or more hardware.
  • the hardware may include an electronic circuit, a device including the electronic circuit, or the like.
  • each storage device (memory) among the plurality of storage devices (memory) stores only a part of the data. It may be stored or the entire data may be stored.

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Abstract

L'invention concerne une technique de détection d'une anomalie dans des données cibles de détection avec une grande précision. Un aspect de la présente invention concerne un procédé de génération de modèle comprenant : une étape dans laquelle un ou plusieurs processeurs entrent des données cibles de détection dans un modèle de détection d'anomalie et acquièrent une carte de degré d'anomalie à partir du modèle de détection d'anomalie ; une étape dans laquelle le ou les processeurs acquièrent une étiquette de région contenant une paire de données de région indiquant la région dans les données cibles de détection et une valeur d'étiquette indiquant si la région est normale ou anormale ; une étape dans laquelle le ou les processeurs acquièrent un score de degré d'anomalie à partir de la carte de degré d'anomalie et des données de région ; une étape dans laquelle le ou les processeurs acquièrent une valeur de perte des données cibles de détection à partir du score de degré d'anomalie et de la valeur d'étiquette ; et une étape dans laquelle le ou les processeurs mettent à jour des paramètres du modèle de détection d'anomalie en fonction de la valeur de perte.
PCT/JP2021/002956 2020-01-30 2021-01-28 Procédé de génération de données, dispositif de génération de données, procédé de détection d'anomalie, dispositif de détection d'anomalie, et programme WO2021153646A1 (fr)

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JP2019096006A (ja) * 2017-11-21 2019-06-20 キヤノン株式会社 情報処理装置、情報処理方法

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* Cited by examiner, † Cited by third party
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JP2019096006A (ja) * 2017-11-21 2019-06-20 キヤノン株式会社 情報処理装置、情報処理方法

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