Foreign matter detection device and foreign matter detection method

Abstract

To provide a foreign matter detection device and a foreign matter detection method that can easily detect foreign matter on an object.SOLUTION: A foreign matter detection device includes: an input section for acquiring images; a processor for analyzing images; and an output section for outputting a control signal from the processor. The processor controls a heating device so as to start heating an object via the output section, and controls the heating device so as to stop heating after a predetermined time after starting heating. The processor controls a photographing device so as to generate multiple temperature image data by photographing a photographing area including at least one portion of the object in time series during a period after stopping heating the object. The processor acquires the multiple temperature image data via the input section, generates an analysis image corresponding to a temperature change in the photographing area by performing analysis processing to the multiple temperature image data, and detects foreign matter on the basis of the analysis image.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a foreign object detection device and a foreign object detection method. [Background technology]

[0002] Patent Document 1 discloses a method for estimating the flow state of a liquid. This method includes the steps of applying heat to the liquid, acquiring a temperature distribution of the liquid to which the heat has been applied, and estimating the flow state of the liquid based on the acquired temperature distribution. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2021 / 261472 Summary of the Invention [Problem to be solved by the invention]

[0004] In order to maintain the quality of products, it is necessary to detect foreign objects that are present on objects such as products due to adhesion or the like.

[0005] The present disclosure provides a foreign object detection device and a foreign object detection method that facilitates detection of a foreign object on an object. [Means for solving the problem]

[0006] One aspect of the present disclosure is a foreign object detection device that detects a foreign object on an object, comprising: an input unit for acquiring an image; A processor; an output unit that outputs a control signal from the processor; The processor Controlling the heating device via the output unit to start heating the object; Controlling the heating device so as to stop heating after a predetermined time from the start of heating; controlling the imaging device to capture images of an imaging region including at least a portion of the object in a time series during a period after heating of the object is stopped, thereby generating a plurality of temperature image data; A plurality of temperature image data are acquired via an input unit, Analyzing the plurality of pieces of temperature image data to generate an analysis image corresponding to a temperature change in the photographed area; Foreign objects are detected based on the analyzed images.

[0007] One aspect of the present disclosure is a foreign object detection method for detecting a foreign object on an object, comprising: Initiating heating of the object by a heating device; Stopping the heating of the object by the heating device; A step of capturing images of an imaging area including at least a part of the object in a time series during a period after heating of the object is stopped by an imaging device to generate a plurality of temperature image data; acquiring a plurality of temperature image data; A step of performing an analysis process on the plurality of temperature image data to generate an analysis image corresponding to a temperature change in the photographed area; and detecting foreign matter based on the analysis image. Effect of the Invention

[0008] According to the foreign object detection device and foreign object detection method disclosed herein, it is possible to easily detect a foreign object on an object. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing a configuration example of an inspection system according to a first embodiment. [Diagram 2] A block diagram showing an example of the configuration of the inspection device shown in FIG. [Diagram 3] A graph showing the change in temperature of oil and aluminum over time when an aluminum workpiece with oil on it is flash heated. [Figure 4] 2 is a flowchart illustrating the operation of the inspection apparatus of FIG. 1; [Diagram 5]A flowchart illustrating details of the temperature image acquisition process shown in FIG. [Figure 6] FIG. 13 is a schematic diagram illustrating a temperature image shown by temperature image data acquired after heating is stopped; [Figure 7] A flowchart illustrating details of the object detection process shown in FIG. [Figure 8] FIG. 13 is a schematic diagram illustrating a reference frame generated by photographing an area near the lowest temperature; [Figure 9] FIG. 13 is a schematic diagram illustrating a reference frame generated by photographing an area near the highest temperature. [Figure 10] Schematic diagram illustrating a difference image [Figure 11] FIG. 11 is a schematic diagram illustrating a binarized image obtained by performing a filter process and a binarization process on the difference image in FIG. 10; [Figure 12] 11 is a flowchart illustrating a process for detecting an object according to a second embodiment of the present invention; [Figure 13] FIG. 1 is a schematic diagram illustrating a binarized image obtained by performing filtering and binarization on a phase image with a set frequency of 0.7 Hz. [Figure 14] FIG. 1 is a schematic diagram illustrating a binarized image obtained by performing filtering and binarization on a phase image with a set frequency of 0.07 Hz. [Figure 15] Graph showing the change in temperature of oil and SUS304 over time when a workpiece made of SUS304 with oil attached thereto according to the first modified example is flash heated. [Figure 16] A schematic diagram showing temperature image data generated by photographing an area of ​​SUS304 near the highest temperature [Figure 17] A difference image showing the difference between the temperature image data of FIG. 16 and the reference frame. [Figure 18] FIG. 18 is a schematic diagram illustrating a binarized image obtained by performing a filter process and a binarization process on the difference image in FIG. 17; [Figure 19] A schematic diagram illustrating a binarized image obtained by performing filtering and binarization on a phase image of SUS304 with a set frequency of 0.5 Hz. [Figure 20] A graph showing the temperature changes over time of oil and SPCC steel when a workpiece made of SPCC steel with oil on it is flash heated. [Figure 21] A schematic diagram illustrating temperature image data 41 generated by photographing an area of ​​SPCC steel near the highest temperature. [Figure 22] A difference image showing the difference between the temperature image data of FIG. 21 and the reference frame. [Diagram 23] FIG. 1 is a schematic diagram illustrating a binarized image obtained by performing filtering and binarization on a phase image of SPCC steel with a set frequency of 1.0 Hz. [Figure 24] FIG. 1 is a schematic diagram illustrating a binarized image obtained by performing filtering and binarization on a phase image of SPCC steel with a set frequency of 0.5 Hz. [Diagram 25] A graph showing the temperature changes over time of oil and SPCC steel when rubber with oil on it is flash heated. [Figure 26] A schematic diagram illustrating temperature image data 45 generated by photographing a region of rubber near the highest temperature. [Figure 27] A difference image showing the difference between the temperature image data of FIG. 26 and the reference frame. [Figure 28] FIG. 28 is a schematic diagram illustrating a binarized image obtained by performing a filtering process and a binarization process on the difference image of FIG. 27; [Figure 29] FIG. 1 is a schematic diagram illustrating a binarized image obtained by performing a filter process and a binarization process on a phase image related to rubber with a set frequency of 0.5 Hz; [Diagram 30] Graph showing the change in temperature of oil and aluminum over time when aluminum with oil attached thereto according to the second modified example is heated in steps. [Diagram 31] Graph showing the change in temperature of oil and rubber over time when the rubber with oil attached thereto according to the second modified example is heated in steps DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, the embodiments will be described in detail with reference to the drawings as appropriate. However, more detailed explanation than necessary may be omitted. For example, detailed explanation of already well-known matters or duplicate explanation of substantially the same configuration may be omitted. This is to avoid the following explanation becoming unnecessarily redundant and to facilitate understanding by those skilled in the art. Note that the inventor provides the accompanying drawings and the following explanation so that those skilled in the art can fully understand the present disclosure, and does not intend to limit the subject matter described in the claims by them.

[0011] An inspection system according to an embodiment will be described below with reference to the drawings.

[0012] [1. First Embodiment] [1-1. Configuration] [1-1-1. Inspection system configuration] 1 is a block diagram showing a configuration example of an inspection system 1 according to a first embodiment of the present disclosure. The inspection system 1 includes an inspection device 10, which is an example of a foreign object detection device, an infrared camera 17, an excitation source 18, a control box 15, a power source 16, and an alarm device 19.

[0013] The inspection system 1 can non-destructively detect foreign matter on the object 91, for example, an attachment 92 attached to the object 91, by using an active thermography method in which excitation energy is applied to the object 91 to be inspected and temperature images are taken while the object 91 is heated. For example, the inspection system 1 applies excitation energy to the object 91 by an excitation source 18, and takes temperature images in time series using an infrared camera 17.

[0014] In addition to or instead of the period during which heating is being performed, the inspection system 1 captures images of the object 91 in a time series during the period after heating has stopped. This enables the inspection system 1 to detect whether or not the object 91 has an attachment 92 attached thereto, using the difference in temperature or temperature change between the object 91 and the attachment 92 after heating has stopped.

[0015] The infrared camera 17 captures an image of an area including at least a part of the object 91 in a time series to generate a plurality of temperature image data. The infrared camera 17 includes an infrared sensor that detects infrared rays having a wavelength of, for example, 3 μm to 15 μm. The frame rate of the infrared camera 17 is, for example, 50 Hz (or 50 fps), but is not limited to this.

[0016] The excitation source 18 is an example of a heating device capable of heating the target object 91 and the attached matter 92. For example, it may be a xenon lamp, a laser light source, a vibrator that generates ultrasonic waves, or a coil that generates electromagnetic induction, but is not limited thereto, and may be any energy source capable of radiating energy. The excitation source 18 can perform flash heating (pulse heating) by emitting a flash of light, or step heating by heating in steps, on the target object 91. The wavelength band of the light emitted by the excitation source 18 may be the same as or different from the wavelength band of infrared light that the infrared camera 17 can detect. In FIG. 1, two excitation sources 18 are illustrated, but the number of excitation sources 18 is not limited thereto, and may be one, or three or more.

[0017] The power supply 16 supplies power to the infrared camera 17 and the excitation source 18. The control box 15 includes a control circuit that controls the power supply 16 based on a control signal from the inspection device 10. The control box 15 may control the light emission method, light emission cycle, light emission time, etc. of the excitation source 18.

[0018] The notification device 19 notifies information to the outside. For example, the notification device 19 is controlled by the inspection device 10, and notifies a user of information indicating the detection result of the attachment 92. The notification device 19 may include a visual notification device such as a light source such as an LED, a display, or an indicator. The notification device 19 may also include an auditory notification device such as a speaker.

[0019] [1-1-2. Configuration of the inspection device] Fig. 2 is a block diagram showing an example of the configuration of the inspection device 10 of Fig. 1. The inspection device 10 includes a processor 11, a storage device 12, and an interface 13.

[0020] The processor 11 is composed of a CPU, an MPU, etc., and controls the entire inspection device 10 by executing various programs stored in the storage device 12. The processor 11 controls the excitation source 18 via the control box 15, etc., thereby controlling the start and stop of the heating output of the excitation source 18. The processor 11 also controls the shooting operations of the infrared camera 17, such as the start and stop of shooting. The processor 11 also analyzes temperature image data stored in the storage device 12 and detects attachments 92 on the target object 91, as described below.

[0021] The storage device 12 is a recording medium for recording various information including data and programs required to realize the functions of the inspection device 10. The storage device 12 is realized by, for example, a semiconductor storage device such as a flash memory or a solid state drive (SSD), a magnetic storage device such as a hard disk drive (HDD), or other recording media alone or in combination. The storage device 12 is not limited to a built-in storage device installed in the same housing as the processor 11, and may be, for example, an external storage device or a NAS (network-attached storage) type storage device. The storage device 12 may include a volatile memory such as an SRAM or a DRAM.

[0022] The interface 13 connects the inspection device 10 to external devices such as the infrared camera 17, the control box 15, the alarm device 19, and the excitation source 18. The interface 13 may be a communication circuit that performs data communication in accordance with an existing wired communication standard or wireless communication standard.

[0023] The interface 13 is an example of an input unit that connects the inspection device 10 and the infrared camera 17 in order to input information such as temperature image data from the infrared camera 17 to the inspection device 10. The interface 13 is also an example of an output unit that connects the inspection device 10 to an external device in order to output information such as a control signal from the processor 11 to the external device such as the control box 15, the alarm device 19, and the excitation source 18. Such input and output units may be integrated as an input / output interface 13 as shown in FIG. 2, or may be realized as a plurality of interface circuits.

[0024] [1-2. Operation] [1-2-1. Overall operation] The operation of the inspection device 10 will be described below. The processor 11 of the inspection device 10 according to this embodiment controls the excitation source 18 via the control box 15 so as to start heating both the target object 91 and the attached matter 92, and stop heating a predetermined time after the start of heating. The infrared camera 17 starts photographing the target object 91 when heating starts or before heating, and continues photographing until a predetermined period has elapsed even after heating stops.

[0025] Due to differences in thermal property values ​​related to heat between the object 91 and the attachment 92, differences occur in the measured values ​​of the temperature change rate during heating (how it heats up) and the temperature change rate after heating is stopped (how it cools down), etc. The thermal property values ​​are, for example, thermal conductivity, thermal diffusivity, and absorptivity.

[0026] 3 is a graph showing the change over time in temperature of the oil and aluminum when an aluminum workpiece, which is an example of a target object 91, having oil, which is an example of an attachment 92, attached thereto, is flash heated. The heating start time is t1, and the heating time is tH. tH is 0.01 to 1 second, for example, 0.1 second. Since there is a difference in thermal conductivity between the oil and the aluminum, as shown in FIG. 3, a difference appears in the temperature and the temperature change rate between the oil and the aluminum after heating is stopped, especially immediately thereafter.

[0027] Therefore, by analyzing the temperature image data of the aluminum with oil attached thereto, it is possible to distinguish between the portion in the temperature image in which the aluminum is shown and the portion in which the oil is shown. In this manner, the processor 11 can detect whether or not the object 91 has an attachment 92 attached thereto by analyzing a plurality of temperature image data sets acquired from the infrared camera 17.

[0028] 4 is a flowchart illustrating an example of the operation of the inspection device 10. Each process shown in this flow is executed by the processor 11 of the inspection device 10, for example.

[0029] 4, the processor 11 executes a temperature image acquisition process S1 and an object detection process S2. The temperature image acquisition process S1 and the object detection process S2 will be described in detail later.

[0030] When the processor 11 detects that the object 91 has the adhesion 92 attached thereto (Yes in S3), it causes the notification device 19 to perform a notification operation (S4). This allows the user to know that the object 91 has the adhesion 92 attached thereto. The notification operation includes, for example, emitting a warning sound from a speaker, and turning on or blinking a light source such as an LED. Alternatively, the processor 11 may cause information indicating the analysis result, the detection result, etc. to be displayed on a display, which is an example of the notification device 19.

[0031] Next, the processor 11 stores information indicating the analysis results, the detection results, etc. in the storage device 12 (S5). The information stored in the storage device 12 is used, for example, to realize traceability. For example, the information stored in the storage device 12 is used to detect whether the state of the object 91 has been changed due to damage, tampering, dirt, etc. during the course of distribution, and if so, the degree of change. Furthermore, the information stored in the storage device 12 may be used to re-learn a trained model, which will be described later.

[0032] In addition, even if the processor 11 does not detect the adhesion 92 (No in S3), it may display information indicating the analysis results, detection results, etc. on a display, which is an example of an alarm device 19, before or after step S5.

[0033] [1-2-2. Temperature image acquisition processing] FIG. 5 is a flowchart illustrating the details of the temperature image acquisition process S1 shown in FIG.

[0034] 5, first, the processor 11 acquires the heating time tH and the imaging time tr (S101). The heating time tH and the imaging time tr are input by, for example, a user and stored in the storage device 12 in advance.

[0035] Next, the processor 11 starts capturing an image of the object 91 by the infrared camera 17 (S102). The start time of capturing is set to 0 (t=0). The infrared camera 17 captures an image of a capturing area including at least a part of the object 91.

[0036] Next, at time t1 (see FIG. 3), the processor 11 controls the excitation source 18 via the control box 15 to start heating the object 91 by the excitation source 18 (S103).

[0037] Next, the processor 11 acquires temperature image data indicating a temperature image corresponding to the temperature of the target object 91 from the infrared camera 17 (S104). The acquired temperature image data is stored in the storage device 12. In step S104, the processor 11 acquires temperature image data captured before the start of heating and temperature image data captured during the start of heating.

[0038] Next, the processor 11 determines whether the time t exceeds the sum of the heating start time t1 and the heating time tH (S105), and continues acquiring temperature image data (S104) until t>t1+tH holds. The processes of steps S104 and S105 are repeated at a predetermined time interval, such as a preset frame acquisition interval of the temperature image.

[0039] If t>t1+tH in step S105, the processor 11 stops heating the object 91 by the excitation source 18 (S106). As shown in steps S103 to 106 above, the processor 11 controls the excitation source 18 to stop heating the object 91 a predetermined time (tH) after the start of heating the object 91.

[0040] Even after heating is stopped, imaging and acquisition of temperature image data are continued until imaging time tr has elapsed from the start of imaging. That is, after heating is stopped in step S106, the processor 11 acquires temperature image data from the infrared camera 17 (S107).

[0041] Fig. 6 is a schematic diagram illustrating a temperature image 30 indicated by the temperature image data acquired in step S107 after heating has been stopped. The temperature image 30 in Fig. 6 is a temperature image obtained by photographing an aluminum workpiece, which is an example of the target object 91, having oil, which is an example of the deposit 92, attached thereto.

[0042] The bright (high brightness) region 30a in the temperature image 30 is the region where the infrared camera 17 is reflected by the surface of the object 91. That is, the imaging element and other parts of the infrared camera 17 itself, which have become hot due to the shooting operation, are reflected in the angle of view (shooting region) of the infrared camera 17. Depending on the arrangement of the excitation source 18, the excitation source 18, which has just emitted light and has not yet cooled down, may be reflected by the surface of the object 91 and be reflected in the temperature image 30.

[0043] 6, the regions 30b and 30c that appear dark (have low brightness) may not be distinguishable by simply analyzing the temperature image 30. Therefore, at the time of obtaining the temperature image 30 in step S107, the attachment 92 is not yet detected.

[0044] 5, after step S107, the processor 11 determines whether the time t has exceeded the photographing time tr (S108), and continues acquiring temperature image data (S107) until the time t has exceeded the photographing time tr. The processes of steps S107 and S108 are repeated at a predetermined time interval, such as a preset frame acquisition interval for temperature images.

[0045] When the processor 11 determines in step S108 that the time t has exceeded the photographing time tr, it controls the infrared camera 17 to end photographing the object 91 (S109).

[0046] [1-2-3. Detection of Adherent Objects] FIG. 7 is a flowchart illustrating the details of the object detection process S2 shown in FIG.

[0047] In FIG. 7, first, the processor 11 determines one of the temperature image data acquired in the temperature image acquisition process S1 as a reference frame (reference temperature image data) (S201). The reference frame is, for example, temperature image data generated by photographing the photographed area before the start of heating (S103). Alternatively, the reference frame may be temperature image data generated by photographing the photographed area after a sufficient time has passed since the stop of heating (S106). In these cases, the reference frame is temperature image data generated by photographing the photographed area (object 91, or object 91 and attached matter 92) at or near the time when the temperature of the photographed area is the lowest. FIG. 8 is a schematic diagram illustrating a reference frame 31 generated by photographing the photographed area near such a lowest temperature. The photographed object in FIG. 8 is an aluminum workpiece with oil attached thereto, similar to FIG. 6.

[0048] In another example, the reference frame is temperature image data generated by photographing the photographed area at or immediately before the heating is stopped in step S106. In this case, the reference frame is temperature image data generated by photographing the photographed area at or near the time when the temperature of the photographed area is the highest. Fig. 9 is a schematic diagram illustrating a reference frame 32 generated by photographing the photographed area near the highest temperature.

[0049] 7, after step S201, the processor 11 calculates the difference between each of the plurality of temperature image data acquired in the temperature image acquisition process S1 and the reference frame determined in step S201 to generate a plurality of difference images (S202). FIG. 10 is a schematic diagram illustrating an example of the difference image 33.

[0050] If there is an attachment 92 on the surface of the object 91 in the photographing area, which has thermal properties different from those of the object 91, a difference occurs in the measured values ​​such as the temperature change rate during heating and after heating is stopped (see FIG. 3). By using a difference image 33 between the temperature image data acquired during heating or after heating is stopped and the reference frame, the temperature change during heating or after heating is stopped and the time of photographing the reference frame for the object 91 and the attachment 92 can be found. Therefore, in step S206 described later, it is possible to accurately detect whether the attachment 92 is attached to the object 91 based on the difference between the temperature change of the attachment 92 and the object 91.

[0051] Moreover, the difference image 33 in FIG. 10 is an image represented by image data generated by subtracting the reference frame 31 captured before the start of heating from the temperature image data captured immediately after the heating is stopped S106.

[0052] When the difference image is an image obtained by subtracting the reference frame 31 from the temperature image data captured immediately after the heating is stopped (S106), noise is reduced compared to the case where temperature image data captured during heating, when the temperature changes rapidly, is used. Therefore, in step S206 described later, it is possible to accurately detect whether or not the object 91 has a substance 92 attached thereto.

[0053] Returning to Fig. 7, the processor 11 extracts a maximum contrast image having the maximum contrast from the plurality of difference images generated in step S202 (S203). Alternatively, the processor 11 may extract a group of images having contrast equal to or greater than a predetermined threshold, and determine one of the group of images as the maximum contrast image. For example, the processor 11 determines the difference image 33 in Fig. 10 as the maximum contrast image.

[0054] Next, the processor 11 performs a filter process on the maximum contrast image extracted in step S203 (S204). The filter process is, for example, a local equalization (smoothing) filter process, a high-pass filter process, or a low-pass filter process. The filter process may include a process for adjusting a tone curve. The processor 11 performs processes such as edge emphasis, shading adjustment, and contrast adjustment by the filter process.

[0055] Next, the processor 11 performs binarization processing on the image after the filter processing in step S204 (S205). Fig. 11 is a schematic diagram illustrating a binarized image 34 obtained by performing the filter processing S204 and the binarization processing S205 on the difference image 33 in Fig. 10. For example, a region 34a that appears white in the binarized image 34 has a pixel value of "1", and a region 34b that appears black has a pixel value of "0". Conversely, the region 34a may have a pixel value of "0", and the region 34b may have a pixel value of "1".

[0056] Returning to FIG. 7, the processor 11 uses the binarized image 34 to detect whether or not a substance 92 is attached to the target object 91 (S206).

[0057] For example, in the case where a pixel having a pixel value "1" corresponds to the attached matter 92, the processor 11 detects that the attached matter 92 is attached to the object 91 when there is a pixel having a pixel value "1" in the photographed area. Alternatively, the processor 11 may detect that the attached matter 92 is attached to the object 91 when there are both pixels having a pixel value "1" and pixels having a pixel value "0" in the photographed area.

[0058] Alternatively, the processor 11 may detect that an object 91 has an attachment 92 attached thereto when the area of ​​an area composed of multiple pixels having a pixel value of "1" (e.g., area 34a in FIG. 11) is equal to or greater than a predetermined value.

[0059] The same applies to the case where a pixel having a pixel value "0" corresponds to the attached matter 92. That is, the processor 11 may detect that the attached matter 92 is attached to the object 91 when there is a pixel having a pixel value "0" in the photographed area, when the area of ​​an area composed of a plurality of pixels having a pixel value "0" is equal to or larger than a predetermined value, etc.

[0060] Alternatively, the processor 11 may input the binarized image 34 to a trained model stored in the storage device 12, and cause the trained model to output a detection result of the attachment 92. Such a trained model is generated, for example, by a supervised learning method in which the model learns the relationship between the binarized image and correct answer information regarding the presence or absence of the attachment 92. One example of such a model is a training model having a neural network structure, for example, a Convolutional Neural Network (CNN). The training of the training model is performed by the processor 11 or another arithmetic circuit, for example, by using an error backpropagation method.

[0061] Instead of step S206, a person such as a user may determine whether or not the object 91 has a foreign substance 92 attached thereto, based on the binarized image .

[0062] [1-3. Effects, etc.] As described above, in this embodiment, the inspection device 10, which is an example of a foreign object detection device, detects the adhering matter 92, which is an example of a foreign object on the object 91. The inspection device 10 includes the interface 13, which is an example of an input unit and an output unit, and the processor 11. The interface 13 acquires an image. The interface 13 outputs a control signal from the processor 11. The processor 11 controls the excitation source 18, which is an example of a heating device, to start heating the object 91 (S103), and controls the excitation source 18 to stop heating a predetermined time after the start of heating (S106). The processor 11 controls the infrared camera 17, which is an example of an imaging device, to capture images of an imaging area including at least a part of the object 91 in time series during a period after the heating of the object 91 is stopped, and generate a plurality of temperature image data, and acquires a plurality of temperature image data via the interface 13 (S102, S104, S107). The processor 11 performs analysis processing on the multiple pieces of temperature image data to generate an analysis image corresponding to the temperature change in the photographed area (S201 to S205), and detects the attachment 92 based on the generated analysis image (S206).

[0063] With the above configuration, the inspection device 10 can easily detect the attached substance 92 on the target object 91.

[0064] Furthermore, the inspection device 10 can easily detect attachments 92 on the target object 91 with high accuracy by using temperature image data captured during the period after heating has stopped, when there is less noise compared to during heating.

[0065] The processor 11 may detect the attachment 92 based on the difference between the temperature change of the attachment 92 and the temperature change of the object 91. Specifically, for example, the processor 11 may generate an analysis image based on the difference between each of the multiple temperature image data and reference temperature image data generated by photographing the photographed area (S202). The reference temperature image data may be information generated by photographing the photographed area before heating of the object 91 is started or stopped, for example.

[0066] [2. Second Embodiment] In the second embodiment, the processor 11 executes an object detection process S2a using a discrete Fourier transform in FIG. 12, instead of the object detection process S2 using a differential method in FIG.

[0067] 12, first, the processor 11 acquires an analysis time and a set frequency (S211). The analysis time and the set frequency are input by, for example, a user and stored in advance in the storage device 12. The analysis time includes, for example, an analysis start time and an analysis end time. Alternatively, instead of the analysis time, the processor 11 may acquire a setting regarding which frames (for example, the 11th to 100th frames, or all frames from the 9th frame onward) to use for analysis after the start of imaging.

[0068] Next, the processor 11 determines an analysis start frame (start frame) based on the analysis time acquired in step S211 (S212). The start frame is, for example, temperature image data generated by photographing the photographed area when, immediately after, or immediately before the heating is stopped in step S106. In this case, the start frame is temperature image data generated by photographing the photographed area at or near the time when the temperature of the photographed area is the highest.

[0069] Next, the processor 11 performs a discrete Fourier transform on the temperature image data captured within the analysis time after the capture of the start frame (S213), and extracts a phase image showing the phase characteristics at the set frequency (S214).

[0070] When the thermal conductivity of the attachment 92 is high, or when the thermal conductivity of the attachment 92 is higher than that of the object 91, the attachment 92 is more easily detected by the attachment detection method according to the present embodiment by setting the set frequency higher. Therefore, for example, when the thermal conductivity of the attachment 92 is higher than a predetermined value, or when the thermal conductivity of the attachment 92 is higher than that of the object 91, the set frequency is set higher than a predetermined threshold frequency. In particular, the greater the difference in thermal conductivity between the attachment 92 and the object 91, the higher the set frequency is set.

[0071] Alternatively or in addition, when the attachment 92 is thin, the attachment 92 is more easily detected by the attachment detection method according to the present embodiment by setting the set frequency higher. Therefore, for example, when the thickness of the attachment 92 is smaller than a predetermined value, the set frequency is set higher than a predetermined threshold frequency. When the attachment 92 is thinner than the object 91 and the difference in thickness between the attachment 92 and the object 91 is equal to or greater than a predetermined value, the set frequency may be set higher than the predetermined threshold frequency. In particular, the greater the difference in thickness between the attachment 92 and the object 91, the higher the set frequency is set.

[0072] If the deposit 92 is a highly viscous liquid, it is likely to become thick when it adheres to the object 91. Conversely, if the viscosity of the deposit 92 is low, it is likely to become thin when it adheres to the object 91. Therefore, for example, the set frequency may be set higher than a predetermined threshold frequency when the viscosity of the deposit 92 expected to adhere to the object 91 is lower than a predetermined value.

[0073] After step S214, the processor 11 performs filter processing on the phase image extracted in step S214 (S215). The filter processing is, for example, local equalization (smoothing) filter processing, high-pass filter processing, or low-pass filter processing. The filter processing may include a process for adjusting a tone curve. The filter processing may also include a background removal process. The processor 11 performs processes such as edge emphasis, shading adjustment, and contrast adjustment through the filter processing.

[0074] Next, the processor 11 performs binarization processing on the image after the filter processing in step S215 (S216). Fig. 13 is a schematic diagram illustrating a binarized image 35 obtained by performing the filter processing S215 and the binarization processing S216 on the phase image obtained in step S214 when the set frequency is 0.7 Hz. As in the first embodiment, the deposit 92 is oil, and the target object 91 is an aluminum workpiece. For example, a region that appears white in the binarized image 34 in Fig. 13 has a pixel value of "1", and a region that appears black has a pixel value of "0".

[0075] Since there is a relationship between the set frequency and the thermal conductivity or thickness of the attachment 92 as described above, if the set frequency is different, the states of the phase image and the binary image may also be different. Fig. 14 is a schematic diagram illustrating a binary image 36 obtained by performing a filter process S215 and a binary process S216 on the phase image obtained in step S214 when the set frequency is 0.07 Hz.

[0076] Returning to Fig. 12, the processor 11 uses the binarized image to detect whether or not an object 91 has an attached substance 92 attached thereto (S217). For example, the processor 11 detects an attached substance 92 when an area of ​​a region consisting of a plurality of pixels having a pixel value of "1" (e.g., the region 35a in Fig. 13 or the region 36a in Fig. 14) is equal to or greater than a predetermined value. For example, the processor 11 determines that the region 35a or the region 36a is the attached substance 92, and the surrounding region 35b or the region 36b is the object 91.

[0077] Steps S216 and S217 may be similar to steps S205 and S206 in FIG. 7, respectively.

[0078] 3. Other embodiments Although the embodiment of the present disclosure has been described in detail above, the above description is merely an example of the present disclosure in every respect. Various improvements and modifications can be made without departing from the scope of the present disclosure. For example, the following modifications are possible. The following modifications can be combined as appropriate.

[0079] [3-1. First modified example] [3-1-1.SUS304] In the first and second embodiments, the example in which the object 91 is an aluminum workpiece has been described, but the object 91 may be an austenitic stainless steel (e.g., SUS304). Fig. 15 is a graph showing the change in temperature of oil and SUS304 over time when a workpiece made of SUS304 with oil attached thereto is flash heated.

[0080] Comparing this with the changes in temperature of the oil and aluminum over time in FIG. 3, in FIG. 15 the difference in temperature and temperature change rate between the oil and SUS304 is more significant after a certain amount of time has passed since heating was stopped (t>0.14) than when heating was stopped (around t=0.12 seconds) or in the period immediately thereafter (t=0.12 to 0.14 seconds).

[0081] Therefore, the time point at which the reference frame and / or temperature image data is to be used may be changed depending on the thermal property values ​​of the object 91 and the attached matter 92, the change over time of the temperature during and after heating, etc. Also, the analysis time and the set frequency for the discrete Fourier transform may be changed.

[0082] Below, with reference to Figures 16 to 19, it will be explained that even when the target object 91 is a workpiece made of SUS304, the adhesion 92 can be detected by using the differential method corresponding to embodiment 1 and the discrete Fourier transform corresponding to embodiment 2.

[0083] Fig. 16 is a schematic diagram illustrating temperature image data 37 generated by photographing an area of ​​SUS304 near the maximum temperature. Fig. 17 is a difference image 38 (see S202 in Fig. 7) showing the difference between the temperature image data 37 and the reference frame. Fig. 18 is a schematic diagram illustrating a binarized image 39 obtained by performing the filter process S204 and binarization process S205 in Fig. 7 on the difference image 33 in Fig. 17. Using the binarized image 39, the processor 11 can detect the presence of an attachment 92 in the area 39a in Fig. 18 (S206).

[0084] Fig. 19 is a schematic diagram illustrating a binarized image 40 obtained by performing a filter process S215 and a binarization process S216 on the phase image obtained in step S214 of Fig. 12 when the set frequency is 0.5 Hz. The processor 11 can use the binarized image 40 to detect the presence of an attachment 92 in the region 40a.

[0085] [3-1-2.SPCC steel] The target object 91 may be cold rolled steel (SPCC steel). Fig. 20 is a graph showing the change in temperature of oil and SPCC steel over time when a workpiece made of SPCC steel with oil attached thereto is flash heated.

[0086] Fig. 21 is a schematic diagram illustrating temperature image data 41 generated by photographing an area of ​​SPCC steel near the highest temperature. Fig. 22 is a difference image 42 showing the difference between the temperature image data 41 and a reference frame. The processor 11 can detect the presence of an attachment 92 in an area 42a in Fig. 22 by using a binarized image obtained by binarizing the difference image 42.

[0087] Fig. 23 is a schematic diagram illustrating a binarized image 43 obtained by performing a filter process S215 and a binarization process S216 on the phase image obtained in step S214 in Fig. 12 when the set frequency is 1.0 Hz. The processor 11 can use the binarized image 43 to detect the presence of an attachment 92 in an area 43a.

[0088] Fig. 24 is a schematic diagram illustrating a binarized image 44 obtained by performing a filter process S215 and a binarization process S216 on the phase image obtained in step S214 in Fig. 12 when the set frequency is 0.5 Hz. The processor 11 can use the binarized image 44 to detect the presence of an attachment 92 in an area 44a.

[0089] [3-1-3. Rubber] The object 91 may be rubber. Fig. 25 is a graph showing the change in temperature of oil and SPCC steel over time when rubber with oil attached thereto is flash heated.

[0090] Compared with the temperature changes over time of the oil and aluminum in Fig. 3, in Fig. 25, there is a difference in temperature and temperature change rate between the oil and rubber not only after heating is stopped but also during heating. In such a case, the processor 11 may control the infrared camera 17 to capture images of the object 91 in time series during the heating period instead of or in addition to the period after heating is stopped to generate multiple temperature image data.

[0091] Fig. 26 is a schematic diagram illustrating temperature image data 45 generated by photographing an area of ​​rubber near the highest temperature. Fig. 27 is a difference image 46 showing the difference between the temperature image data 45 and the reference frame. Fig. 28 is a schematic diagram illustrating a binarized image 47 obtained by performing the filter process S204 and binarization process S205 of Fig. 7 on the difference image 46 of Fig. 27. The processor 11 can detect the presence of attachment 92 in areas 47a and 47b of Fig. 28 using the binarized image 47.

[0092] Fig. 29 is a schematic diagram illustrating a binarized image 48 obtained by performing a filter process S215 and a binarization process S216 on the phase image obtained in step S214 of Fig. 12 when the set frequency is 0.5 Hz. The processor 11 can detect the presence of attachments 92 in regions 48a and 48b by using the binarized image 48.

[0093] [3-2. Second modified example] In the first and second embodiments, the example of flash heating of the object 91 has been described, but the heating method is not limited to this. For example, a step heating method may be adopted as the heating method. The step heating method is a heating method in which a constant amount of heat is applied to the object 91 for a longer period of time than the heating time of the flash heating method.

[0094] Figure 30 is a graph showing the change in temperature of the oil and aluminum over time when aluminum with oil attached is heated in steps. Figure 31 is a graph showing the change in temperature of the oil and rubber over time when rubber with oil attached is heated in steps. The graphs in Figures 30 and 31 show data obtained by performing step heating for about 5 seconds from t=0.

[0095] In the step heating method, in addition to the excitation source 18, a relatively inexpensive infrared lamp, an electric resistance, or the like can be used as the heat source.

[0096] [3-3.Third modified example] In the second embodiment, an example is described in which a phase image is extracted by performing a discrete Fourier transform on temperature image data, but what is extracted by the discrete Fourier transform is not limited to a phase image. For example, the processor 11 may extract an amplitude image showing the amplitude characteristics of data after the discrete Fourier transform, a real part image showing the real part of data after the discrete Fourier transform, which is a complex number, or an imaginary part image showing the imaginary part.

[0097] [3-4. Fourth Modification] In the first and second embodiments, the example in which the deposit 92 is oil has been described, but the deposit 92 is not limited to this. For example, the deposit 92 may be water, gasoline, battery fluid, grease, ink, or paint.

[0098] [3-5. Fifth Modification] In embodiment 1, an example is described in which the thermal conductivity, thickness, or viscosity of the attachment 92 is compared with a predetermined value or the thermal conductivity, thickness, or viscosity of the object 91, and the set frequency is determined based on the comparison result, but the method of determining the set frequency is not limited to this.

[0099] For example, a predetermined threshold frequency for a reference deposit having a predetermined composition (oil, water, etc.) and a predetermined thickness may be set in advance, and the set frequency may be determined based on a comparison result between this reference deposit and the deposit 92. For example, if the thermal conductivity of the deposit 92 is higher than that of the reference deposit, the set frequency is set higher than the predetermined threshold frequency. For example, if the deposit 92 is thinner than the reference deposit, the set frequency is set higher than the predetermined threshold frequency. For example, if the viscosity of the deposit 92 is lower than the viscosity of the reference deposit, the set frequency is set higher than the predetermined threshold frequency.

[0100] [4. Aspects] The following provides examples of aspects of the present disclosure.

[0101] <Aspect 1> A foreign object detection device that detects a foreign object on an object, comprising: an input unit for acquiring an image; A processor for analyzing the image; an output unit that outputs a control signal from the processor; The processor, Controlling a heating device via the output unit to start heating the object; Controlling the heating device so as to stop the heating after a predetermined time from the start of the heating; Controlling an image capturing device to capture images of an image capturing region including at least a portion of the object in a time series during a period after heating of the object is stopped, thereby generating a plurality of temperature image data; acquiring the plurality of temperature image data via the input unit; performing an analysis process on the plurality of temperature image data to generate an analysis image corresponding to a temperature change in the photographed area; Detecting the foreign object based on the analysis image. Foreign object detection device.

[0102] <Aspect 2> 2. The foreign object detection device of claim 1, wherein the processor detects the foreign object based on a difference between a temperature change of the foreign object and a temperature change of the target object.

[0103] <Aspect 3> 3. The foreign object detection device according to claim 1, wherein the processor generates the analysis image based on a difference between each of the plurality of temperature image data and reference temperature image data generated by photographing the photographing area.

[0104] <Aspect 4> 4. A foreign object detection device according to aspect 3, wherein the reference temperature image data is information generated by photographing the photographing area before heating of the object is started or stopped.

[0105] <Aspect 5> 3. The foreign object detection device according to aspect 1 or 2, wherein the processor performs a discrete Fourier transform on the plurality of temperature image data to generate the analysis image.

[0106] <Aspect 6> 6. A foreign object detection device according to claim 5, wherein the analysis image is a phase image at a predetermined frequency of the plurality of temperature image data subjected to a discrete Fourier transform.

[0107] <Aspect 7> 7. The foreign object detection device according to claim 6, wherein the predetermined frequency is set to be higher than a predetermined threshold frequency when the thermal conductivity of the foreign object is higher than a predetermined value or the thermal conductivity of the target object.

[0108] <Aspect 8> The foreign object detection device according to any one of aspects 1 to 7, further comprising an alarm device that notifies information indicating a result of the foreign object detection.

[0109] <Aspect 9> 9. The foreign object detecting device according to any one of aspects 1 to 8, further comprising a storage device that stores a result of the foreign object detection.

[0110] <Aspect 10> A foreign object detection method for detecting a foreign object on an object, comprising: starting heating of the object with a heating device; Stopping the heating of the object by the heating device; A step of capturing images of an imaging area including at least a part of the object in a time series during a period after heating of the object is stopped by an imaging device to generate a plurality of temperature image data; acquiring the plurality of temperature image data; performing an analysis process on the plurality of temperature image data to generate an analysis image corresponding to a temperature change in the photographed area; detecting the foreign object based on the analysis image. Foreign object detection method. [Industrial Applicability]

[0111] The present disclosure is applicable to a foreign object detection device, a foreign object detection method, and a non-destructive inspection device. [Explanation of symbols]

[0112] 1. Inspection system 10 Inspection equipment 11 Processors 12 Storage device 13 Interface 15 Control Box 16 Power supply 17 Infrared Camera 18 Excitation Source 19. Notification device 91 Object 92 Attachments

Claims

1. A foreign object detection device that detects a foreign object on an object, an input unit for acquiring an image; a processor for analyzing the image; an output unit that outputs a control signal from the processor; The processor: Controlling a heating device via the output unit to start heating the object; controlling the heating device so as to stop the heating after a predetermined time has elapsed since the start of the heating; controlling the photographing device to photograph a photographing area including at least a part of the object in time series during a period after heating of the object has stopped, and to generate a plurality of temperature image data; acquiring the plurality of temperature image data via the input unit; performing an analysis process on the plurality of temperature image data to generate an analysis image corresponding to a temperature change in the photographed area; detecting the foreign matter based on the analysis image; Foreign object detection device.

2. The foreign object detection device according to claim 1 , wherein the processor detects the foreign object based on a difference between a temperature change of the foreign object and a temperature change of the target object.

3. The foreign object detection device according to claim 1 , wherein the processor generates the analysis image based on a difference between each of the plurality of temperature image data and reference temperature image data generated by photographing the photographing area.

4. The foreign object detecting device according to claim 3 , wherein the reference temperature image data is information generated by photographing the photographing area before heating of the object is started or stopped.

5. The foreign object detection device according to claim 1 , wherein the processor performs a discrete Fourier transform on the plurality of temperature image data to generate the analysis image.

6. 6. The foreign matter detecting device according to claim 5, wherein the analysis image is a phase image at a predetermined frequency of the plurality of temperature image data that have been subjected to a discrete Fourier transform.

7. The foreign object detecting device according to claim 6 , wherein the predetermined frequency is set higher than a predetermined threshold frequency when the thermal conductivity of the foreign object is higher than a predetermined value or the thermal conductivity of the target object.

8. The foreign object detection device according to claim 1 , wherein the information indicating the foreign object detection result is notified by an alarm device.

9. The foreign matter detecting device according to claim 1 , further comprising a storage device that stores the foreign matter detection result.

10. A foreign object detection method for detecting a foreign object on an object, comprising: Initiating heating of the object with a heating device; stopping the heating of the object by the heating device; a step of capturing an image of an imaging area including at least a part of the object in time series during a period after heating of the object is stopped using an imaging device to generate a plurality of temperature image data; acquiring the plurality of temperature image data; a step of performing an analysis process on the plurality of temperature image data to generate an analysis image corresponding to a temperature change in the imaging area; detecting the foreign matter based on the analysis image. Foreign object detection method.

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