TW202007970A - Detection method and detection system - Google Patents

Abstract

A detection method includes the following steps. Moving a carrying bed to a testing machine to obtain a mask image group. Providing a test object to the carrying bed, and capturing a test image of the carrying bed and the test object. Setting a detection area according to the mask image group. Then, calculating detected pixels corresponding to the detection area in the test image. If determining that the detected pixels meet a threshold setting condition, adjusting the carrying bed or the test object.

Description

Detection method and detection system

The present disclosure relates to a detection method, especially for scanning a test object on a carrier bed.

Computed tomography (Computed Tomography) is a detection technology that uses multiple X-rays to penetrate an object and reconstruct 3D images in the body through a computer. In addition to being applied outside the human body, it can also be applied to smaller organisms (such as mice).

When using computer tomography to detect organisms, the organisms are placed on a bed and sent to the machine for scanning. Therefore, it is necessary to confirm that the biological placement position is correct, in order to avoid the scanning position error, and the problem that the detection result cannot be interpreted.

One aspect of the present disclosure is an inspection method, which includes the following steps: moving the bed to the inspection machine to obtain a mask image group. Place the test object on the bed. Capture test images of the carrier bed and test objects. Set the detection area according to the mask image group. The calculation detection image corresponds to the detection pixels in the detection area. Determine whether the detection pixel meets at least one threshold setting condition, and if so, adjust the bed or the detection object.

Another aspect of the present disclosure is a detection system, including a load bed, a detection machine, an image detection device, and a processor. The bed is used to carry the test object. The testing machine is used for scanning and testing the carrier bed. When the test object is not placed on the bed, the image detection device is used to capture the mask image group of the bed. When the test object is carried on the bed, the image detector is used to capture the detection images of the bed and the test object. The processor generates a detection area according to the mask image group, and calculates the detection pixels corresponding to the detection area in the detection image. The processor is also used to determine whether the detected pixels meet the threshold setting conditions, and if so, generate an abnormal message to adjust the bed or the test object.

According to this, by calculating the detection pixels corresponding to the detection area in the detection image, it can be judged whether there is an abnormality in the installation state of the carrier bed, so that the carrier bed can be adjusted in real time to ensure that the inspection machine correctly scans the inspection.

In the following, a plurality of embodiments of the case will be disclosed in a diagram. For the sake of clarity, many practical details will be described together in the following description. However, it should be understood that these practical details should not be used to limit the case. That is to say, in some embodiments of the present disclosure, these practical details are unnecessary. In addition, in order to simplify the drawings, some conventional structures and elements will be shown in a simple schematic manner in the drawings.

In this article, when an element is referred to as "connected" or "coupled", it can be referred to as "electrically connected" or "electrically coupled." "Connected" or "coupled" can also be used to indicate that two or more components interact or interact with each other. In addition, although terms such as "first", "second", etc. are used herein to describe different elements, the terms are only used to distinguish elements or operations described in the same technical terms. Unless the context clearly dictates, the term does not specifically refer to or imply order or order, nor is it intended to limit the present invention.

Please refer to FIGS. 1 and 2 for schematic diagrams of the detection system 100 in the present disclosure. The detection system 100 includes a detection machine 110, a bed 120 and an image detection device 130. The detection machine 110 is provided with two mobile platforms 111 and 112. The mobile gantry 111, 112 relatively rotates in the testing machine 110, and are respectively provided with an X-ray emitting device 111A and an X-ray receiving device 112A for scanning and detecting the inside of the testing machine 110. In some embodiments, the inspection machine 110 performs the computed tomography through the X-ray emitting device 111A and the X-ray receiving device 112A, but the disclosure is not limited to this.

The bed 120 is used to carry the detection object 140, and can be sent into the detection machine 110 through the conveying device for scanning detection. In some embodiments, the image detection device 130 (such as a photographic lens) is installed in the inspection machine 110 so that after the bed 120 and the test object 140 are moved into the inspection machine 110, the image detection device 130 It is used to capture the appearance images of the bed 120 and the test object 140. In some embodiments, different specifications (eg, large, medium, or small) of the carrier bed 120 have different detection methods, details of which will be detailed in subsequent paragraphs.

Please refer to FIG. 3. In some embodiments, the detection system 100 further includes a processor 113 and a storage unit 150. The processor 113 is electrically connected to the image detection device 130 and the storage unit 150, and is used to perform calculation according to the image captured by the image detection device 130. The storage unit 150 is used to store the image captured by the image detection device 130 and at least one threshold setting condition 151. The storage unit 150 may be a hard disk in the testing machine 110, or may be an external computer.

Please refer to FIG. 4 for a flowchart of some embodiments of the detection method of the present disclosure. The detection method includes the following steps S403 to S405. In step S401, the processor 113 controls the conveying device (eg, conveying table) on the inspection machine 110 to move the carrier bed 120 to a preset position in the inspection machine 110. At this time, the test object 140 is not yet loaded on the bed 120, and the image detection device 130 captures the appearance of the bed 120 to generate the mask image group M10, and stores the mask image group M10 in the storage unit 150. In some embodiments, the detection system 100 executes the “mask generation process” to generate the mask image group M10. The mask image group M10 includes at least one mask image, for example, an image of the bed 120 in an abnormal state (eg, without an upper cover). In some other embodiments, the mask image group M10 includes a plurality of mask images. The generation method of the mask image group M10 will be described in detail in the following paragraphs.

In step S402, after completing the "mask generation flow", the processor 113 executes the "abnormality detection flow" and sets one or more detection areas according to the mask image group M10. The detection area is a special position of the load bed 120 in an abnormal state. For example, if the bed cover 121 on the load bed 120 is not properly installed, the bed cover 121 may protrude from the load bed 120. Therefore, the detection area may be In the area above the bed 120, if an image is detected in this area, it means that an abnormality has occurred. The details of the detection area will be detailed in subsequent paragraphs.

In step S403, the conveying device on the testing machine 110 is controlled to move the carrier bed 120 outside the testing machine 110, and a user (eg, a testing person) places the test object 140 on the carrier bed 120. In step S404, the carrier bed 120 is moved to the same predetermined position in the inspection machine 110 again, and the appearance of objects on the carrier bed 120 and the test object 140 is captured by the image detection device 130 for detection Image D10.

In step S405, the processor 113 calculates the detection pixels corresponding to the detection area in the detection image D10. In step S406, the processor 113 determines whether the detected pixel meets the threshold setting condition 151. If the determination result is yes, it indicates that the state of the bed 120 corresponding to the detected image D10 is incorrect. In step S407, the detection system 100 will generate Abnormal messages (such as the message "The bed cover is not installed") to adjust the bed 120 or the test object 140 on the bed according to the abnormal message. If not, it means that the carrier bed 120 and the test object 140 on it pass the test and the setting method is correct. In step S408, the test machine 110 will start to perform scanning.

According to this, since in step 401, the user captures the mask image group M10 for the carrier bed 120 in a known state, the user can pass the aforementioned steps before scanning the detection object 140 through the detection machine 110 S403 to S406, the detection image D10 is obtained first, and then the detection image D10 corresponds to the detection pixels in the detection area to determine whether the setting of the bed 120 or the detection object 140 is abnormal, and accordingly the bed 120 is checked in real time. Or adjust the amount or position of the detection object 140. The detection system 100 adjusts the carrier bed 120 or the detection object 140 according to the detection pixels. The detection machine 110 can automatically determine whether the carrier bed and the bed cover are properly closed, or the correct placement and size of the detection object to avoid An important element inside the object collision detection system 100.

In some embodiments, the calculation operations and determination operations in the foregoing steps S405 and S406 are performed by the processor 113 in the testing machine 110, but the disclosure is not limited thereto. In some other embodiments, the testing machine 110 can also be connected to a server or an external computer to perform calculations through the server or an external computer.

In some embodiments, the threshold setting condition 151 may include a plurality of thresholds, each corresponding to a different abnormal state. For example, when the detection system 100 calculates the detection pixels corresponding to the detection area of the detection image D10, the processor 113 is used to calculate the number of pixels in the detection pixels. If the number of pixels exceeds the threshold, it means that the status of the carrier bed 120 corresponding to the detection image D10 is incorrect. At this time, the detection system 100 will generate an abnormal message (such as: "the bed cover is not installed" and other messages). Abnormal information, readjust the amount or position of the test object 140 on the carrier bed 120, readjust the position of the bed cover on the carrier bed 120, or install a bed cover on the carrier bed 120. The method for the detection system 100 to determine the detection pixel and the threshold setting condition 151 will be described in detail in subsequent paragraphs.

Here, the step M10 of obtaining the mask image group will be described as follows. In some embodiments, the mask image group M10 includes an initial image M11 of “the bed 120 is in a correctly set state”, and the processor 113 is used to generate the mask image group M10 corresponding to the initial image M11. One or more masked images in abnormal state. For example, the detection system 100 will first obtain the appearances under abnormal conditions such as "no cover", "convex cover", and "bed cover skew" in the "mask generation process" through the image detection device 130. The difference between the appearance image and the initial image M11 is calculated to obtain images in different abnormal states, and is set as the mask image in the mask image group M10.

Specifically, in some embodiments, the mask image group M10 includes an uncovered mask image M12, a convex mask image M13, and a skew mask image M14. The uncovered mask image M12 corresponds to the appearance of the bed 120 without the bed cover 121 (eg, lack of pixel area); the convex cover mask image M13 corresponds to the appearance of the bed cover 121 when it protrudes from the bed 120 ( For example: the projected pixel area); the skew mask image M14 corresponds to the appearance when the skew angle is maintained between the bed cover 121 and the bed 120.

Here, according to the large, medium and small carrier beds, the corresponding detection methods are explained respectively. The large and medium-sized carrier bed 120 includes a bed cover 121. When detecting the large and medium-sized carrier bed 120, its purpose is to determine whether the bed cover 121 is set correctly. Here, the generation methods of the plurality of mask images of the mask image group M10 (that is, the uncovered mask image M12, the convex mask image M13, and the skew mask image M14) are described as follows.

As shown in FIGS. 1, 5A to 5C, in the “mask generation process”, when the carrier bed 120 is outside the inspection machine 110, the image detection device 130 captures the internal image of the inspection machine 110. Since there is no detection object 140 in the detection machine 110 at this time, the detected internal image should be a completely black screen.

Next, the carrier bed 120 is moved into the inspection machine 110, so that the image detection device 130 captures the first appearance image of the carrier bed 120 in the correctly set state, that is, the state in which the carrier bed 120 and the bed cover 121 are correctly adhered ( For example: in the center of the completely black screen, the area of the bed will be displayed in white pixels). The processor 113 of the inspection machine 110 compares the difference between the first appearance image and the internal image, and treats the internal image as the background color, which is removed from the first appearance image to reduce unnecessary noise interference. The processor 113 sets the image obtained after the aforementioned processing as an initial image M11 in the mask image group M10.

After the initial image M11 is generated, the bed cover 121 on the bed 120 is removed, and the bed 120 is moved to the inspection machine 110, so that the image detection device 130 captures the second time when the bed 120 does not have the bed cover 121 Appearance image 51. In some embodiments, the image detection device 130 removes the same pixels in the second appearance image 51 as the internal image (ie, removes the background color). Next, the difference between the second appearance image 51 and the initial image M11 is compared, and a difference operation is performed to generate the uncovered mask image M12.

Referring to FIG. 6, after completing the “mask generation process”, the detection system 100 will execute the “abnormality detection process” on the carrier bed 120 and the test object 140 to confirm whether the setting state of the carrier bed 120 is correct. In the "abnormality detection process", the testing machine 110 moves the bed 120 and the test object 140 to the testing machine 110, so that the image detection device 130 captures the detection image D11. Please refer to FIG. 6, in some embodiments, after the image detection device 130 captures the detection image D11, it will first remove the area with the same pixel as the internal image from the detection image D11 (ie, remove the background color ).

Next, as shown in FIG. 6, when the “abnormality detection flow” is executed, the processor 113 of the detection machine 110 sets the detection area R1 according to the area area of the uncovered mask image M12 in the mask image group M10. The processor 113 will calculate the number of pixels in the detection image D11 corresponding to the detection area R1. In addition, when determining whether the detected pixel corresponds to the threshold setting condition 151, the processor 113 will determine whether the number of pixels is less than a threshold in the threshold setting condition 151 (eg, 60% of the area in the detection area R1 is white Pixels). If it is less than the threshold value, it means that the carrier bed 120 has an abnormal state of "no cover", and the bed cover 121 should be provided on the carrier bed 120 according to the abnormal message.

Here, the detection of another abnormal state will be described. Referring to FIGS. 7A-7C, after the initial image M11 is generated in the same manner as in the previous embodiment, the position of the bed cover 121 on the bed 120 is adjusted so that the bed cover 121 protrudes from the bed 120. Next, the carrier bed 120 is moved into the inspection machine 110, and the image detection device 130 captures the third appearance image 71 of the bed cover 121 protruding out of the carrier bed 120 (in some embodiments, the image detection device 130 Similarly, the area of the third appearance image 71 with the same pixel as the internal image will be removed). The difference between the third appearance image 71 and the initial image M11 is compared, and a difference operation is performed to generate a convex mask image M12.

Next, as shown in FIG. 8, when the “abnormality detection flow” is executed, the processor 113 of the detection machine 110 sets the detection area R2 according to the area area of the mask image group M10 that covers the mask image M12. The processor 113 will calculate the number of pixels in the detection image D12 corresponding to the detection area R2. In addition, when determining whether the detected pixel corresponds to the threshold setting condition 151, the processor 113 will determine whether the number of pixels is greater than another threshold in the threshold setting condition 151 (eg, a 10% area in the detection area R2 has White pixels). If it is greater than the threshold value, it means that the carrier bed 120 has an abnormal state of "convex cover", and the position of the bed cover 121 on the carrier bed 120 should be adjusted according to the abnormal message.

Here, another abnormal state that may occur in the "medium-sized carrier bed" is explained. Please refer to FIGS. 9A-9C. After the initial image M11 is generated in the same manner as the previous embodiment, the position of the bed cover 121 on the bed 120 is adjusted so that the bed cover 121 and the bed 120 are kept at a skew angle. Next, the carrier bed 120 is moved into the inspection machine 110 to enable the image detection device 130 to capture the fourth appearance image 91 of the carrier bed 120 and the bed cover 121 at a skewed angle (in some embodiments, the image detection device 130 will remove the area of the fourth appearance image 91 that has the same pixel as the internal image). When the bed cover 121 is skewed, the pixel area and distribution area of the fourth appearance image 91 will be significantly different from the initial image M11. By comparing the difference between the fourth appearance image 91 and the initial image M11, and performing a difference calculation, a skew mask image M14 can be generated.

As shown in FIG. 10, when the "abnormality detection process" is executed, the processor 113 of the detection machine 110 sets the detection area R3 according to the area area of the skewed mask image M14 in the mask image group M10. The processor 113 will calculate the number of pixels in the detection image D13 corresponding to the detection area R3. In addition, when determining whether the detected pixel corresponds to the threshold setting condition 151, the processor 113 will determine whether the number of pixels is greater than another threshold value in the threshold setting condition 151 (eg, a 10% area in the detection area R3 has White pixels). If it is greater than the threshold value, it means that the carrier bed 120 has an abnormal state of "skew", and the position of the bed cover 121 on the carrier bed 120 should be adjusted according to the abnormal message.

Accordingly, after the initial image M11, the uncovered mask image M12, the convex mask image M13, and the skew mask image M14 are generated, the processor 113 of the inspection machine 110 can execute the "abnormality detection process". By comparing the mask image group M10 and the detection image D10, the abnormal state of the bed 120 is determined. In some embodiments, when performing the "abnormality detection process", the processor 113 of the inspection machine 110 first converts the inspection image D10 and the uncovered mask image M12, the convex mask image M13 and the mask image group M10 in the mask image group M10. The skew mask images M14 are sequentially compared to confirm which abnormal state the carrier bed 120 is in.

Please refer to FIG. 11 for a schematic diagram of the detection method of the present disclosure applied to a “small carrier bed”. In this embodiment, the inspection system 200 includes an inspection machine 210, a carrier bed 220, and an image detection device 230. The small carrier bed 220 does not have a bed cover, but carries the inspection object through the groove 221. When detecting "small carrier beds", the detection method can also include "mask generation process" and "abnormality detection process". In some embodiments, during the "mask generation process", the processor of the inspection machine 200 can capture the image of the carrier bed 220 in the correct state through the image detection device 230 as a mask image group. The initial image.

In some embodiments, the image detection device 230 is provided in the detection machine 210 and is used to continuously detect the image in the image capture area Rt until the bed 220 completely passes through the image capture area Rt. After receiving the images, the processor of the inspection machine 210 accumulates the images in the image capturing area Rt to generate a complete image corresponding to the bed 220. Similar to the previous embodiment, in some embodiments, the image detection device 230 removes the area with the same pixel as the internal image from the captured image (ie, removes the background color) to generate a corresponding to the carrier bed The initial image of 220.

As shown in FIGS. 12A and 12B, in some embodiments, the width D2 of the test object 240 on the bed 220 is greater than the width D1 of the bed 220, and the image detection device 230 captures the image in the image capture area Rt , And the accumulated result will be shown as image 320 in Figure 12B. The aforementioned “accumulation” means that the detection system 100 will capture the outline of the image. Therefore, although the detection object 240 in FIG. 9A only occupies the middle section of the bed 220, the lower half of the detected image still has Width D2. In some other embodiments of the present disclosure, the image detection device 230 can generate the initial image, the uncovered mask image, the convex mask image, the skew mask image, and the detected image in this way.

Since the small carrier bed 220 does not have a bed cover, the mask image group includes the initial image M11, but does not need to include "no cover", "convex cover" or "bed cover skew" and other abnormal state mask images. During the "abnormality detection process", the image detection device 230 can capture the images of the load bed 220 and the test object 240 on it through the same principle as shown in FIGS. 12A and 12B to obtain the detection images (ie, normal state (The images of the load bed 220 and the test object 240 below are shown in FIG. 12A). The processor of the detection machine 210 sets a detection area based on the area of the initial image M11, and calculates the detection pixels corresponding to the detection area in the detection image.

In some embodiments, since the image detection device 230 continuously captures images in the image capture area Rt, if the processor accumulates images, it finds that the captured detection images correspond to the detection If the detected pixels in the area exceed the threshold (eg, more than 10% are white pixels), the detection machine 210 can interrupt the accumulation process and directly generate an abnormal message. In other words, the processor can simultaneously calculate the detection pixels corresponding to the detection area in the detection image while accumulating the images in the image capturing area Rt. For example, if the detection area is the "width D1" of the bed 220, once the processor determines that the area or area width of the detected pixel is greater than the width D1, an abnormal message will be generated.

The large, medium and small carrier beds are sorted out here to explain the detection steps. Please refer to FIGS. 1 and 13. In step S1001, the setting method of the bed 120 and the bed cover 121 is adjusted, and the bed 120 and the bed cover 121 are moved into the testing machine 110. In step S1002, the image detection device 130 captures images of the bed 120 and the bed cover 121 to generate a mask image in the mask image group M10.

In step S1003, it is determined whether all the mask images in the mask image group M10 (such as the uncovered mask image M12, the convex mask image M13, and the skew mask image M14) have been generated, and each mask image will be Each corresponds to a threshold setting condition. If all the mask images are not stored in the storage unit 150, then return to step S1001. If all the mask images have been stored in the storage unit 150, it means that the "mask generation process" has been completed and the "abnormality detection process" can be executed.

In step S1004, the detection object 140 is placed on the carrier bed 120, and images of the carrier bed 120 and the bed cover 121 are captured by the image detection device 130 to generate a detection image. In step S1005, the detection area is set based on the mask image. In some embodiments, the uncovered mask image M12, the convex mask image M13, and the skew mask image M14 can correspond to a detection area, respectively. In step S1006, it is determined whether the detection pixels corresponding to the detection area of the uncovered mask image M12 in the detection image meet the threshold setting conditions (eg, less than 60%), and if so, step S1009 is executed to generate an abnormal message.

In step S1007, it is determined whether the detection pixels corresponding to the detection area of the convex mask image M13 in the detection image meet the threshold setting conditions (eg, greater than 20%). If yes, step S1009 is executed to generate an abnormal message.

In step S1008, it is further determined whether the detection pixels corresponding to the detection area of the skew mask image M14 in the detection image meet the threshold setting conditions (eg, greater than 20%). If yes, step S1009 is executed to generate an abnormal message. If the determinations in steps S1006 to S1008 are not all, it means that the bed 120 is in a correct state, and the testing machine 210 will perform a scan.

Referring to FIG. 14, when detecting the small carrier bed 220, in step S1101, the carrier bed 220 is moved toward the detection machine 210, and the carrier bed 220 passes through the detection area R1. In step S1102, the image detection device 230 captures and accumulates the image of the bed 220 to generate the initial image in the mask image group M10. In step S1103, the test object 240 is placed on the carrier bed 220, and the carrier bed 220 is moved in the direction of the inspection machine 210. In step S1104, the image detection device 230 captures and accumulates images of the bed 220 to generate a detection image D10. In step S1105, the detection area is set based on the initial image in the mask image group M10.

In step S1106, it is calculated whether the detection pixels corresponding to the detection area in the detection image D10 meet the threshold setting conditions (eg, greater than 10%). If yes, in step S1106, the detection machine 210 will generate an abnormal message. On the contrary, it means that the state of the load bed 220 is normal, and the testing machine 210 will perform scanning.

The various elements, method steps, or technical features in the foregoing embodiments can be combined with each other, and are not limited to the order of text description or the order of drawings presented in this disclosure.

Although the present disclosure has been disclosed as above by way of implementation, it is not intended to limit the content of the present invention. Anyone who is familiar with this skill can make various changes and modifications within the spirit and scope of the present content, so the present invention The protection scope of the content shall be deemed as defined by the scope of the attached patent application.

100‧‧‧ detection system 110‧‧‧ testing machine 111‧‧‧Mobile gantry 111A‧‧‧X-ray emission device 112‧‧‧Mobile gantry 112A‧‧‧X-ray receiving device 113‧‧‧ processor 120‧‧‧ carrier bed 121‧‧‧ Bed cover 130‧‧‧Image detection device 140‧‧‧ test object 150‧‧‧storage unit 200‧‧‧ detection system 210‧‧‧ Testing machine 220‧‧‧ carrier bed 221‧‧‧Cover 230‧‧‧Image detection device 240‧‧‧Detected object M10‧‧‧ Masked image group M11‧‧‧Initial image M12‧‧‧Image without cover M13‧‧‧ convex mask image M14‧‧‧skew mask image D10‧‧‧ Inspection image 51‧‧‧Second appearance image 71‧‧‧ third appearance image 91‧‧‧The fourth appearance image 320‧‧‧Image S401～S408‧‧‧Step S1001～S1009‧‧‧Step S1101～S1106‧‧‧Step D1‧‧‧Width D2‧‧‧Width R1‧‧‧ detection area R2‧‧‧ detection area R3‧‧‧ detection area Rt‧‧‧Image capture area

FIG. 1 is a schematic diagram of a detection system according to some embodiments of the present disclosure. FIG. 2 is a schematic diagram of a testing machine according to some embodiments of the present disclosure. FIG. 3 is a schematic diagram of a detection system according to some embodiments of the present disclosure. FIG. 4 is a flowchart of a detection method according to some embodiments of the present disclosure. 5A-5C are schematic diagrams of the mask generation process in some embodiments of the present disclosure. FIG. 6 is a schematic diagram of an abnormality detection process in some embodiments of the present disclosure. FIGS. 7A-7C are schematic diagrams of a mask generation process in some embodiments of the present disclosure. FIG. 8 is a schematic diagram of an abnormality detection process in some embodiments of the present disclosure. 9A-9C are schematic diagrams of a mask generation process in some embodiments of the present disclosure. FIG. 10 is a schematic diagram of an abnormality detection process in some embodiments of the present disclosure. FIG. 11 is a schematic diagram of a detection system according to some embodiments of the present disclosure. FIG. 12A is a schematic diagram of a carrier bed and a test object according to some embodiments of the present disclosure. FIG. 12B is a schematic diagram of an image captured by an image detection device in some embodiments of the present disclosure. FIG. 13 is a flowchart of a detection method according to some embodiments of the present disclosure. FIG. 14 is a flowchart of a detection method according to some embodiments of the present disclosure.

S401~S408‧‧‧Step

Claims (14)

A detection method, the steps of which include: Move a carrier bed to a testing machine to obtain a mask image group; Place a test object on the carrier bed; Capture a detection image of the loading bed and the detection object; According to the mask image group, set a detection area; Calculating a detection pixel corresponding to the detection area of the detection image; and Determine whether the detected pixel meets at least one threshold setting condition, and if so, adjust the load bed or the detected object. The detection method according to claim 1, wherein the step of determining whether the detected pixel meets the threshold setting condition includes: determining whether the detected pixel is lower than a threshold; When the detected pixel is lower than the threshold, an abnormal message is generated; and According to the abnormal message, a bed cover is provided on the bed. The detection method according to claim 1, wherein determining whether the detected pixel meets the threshold setting condition includes: Determine whether the detected pixel exceeds a threshold; If the detected pixel exceeds the threshold, an abnormal message is generated; and According to the abnormal message, adjust the position of the test object or one of the bed covers of the carrier bed. The inspection method according to claim 1, wherein the step of obtaining the mask image group includes: capturing an internal image of the inspection machine; Capturing a first appearance image of the carrier bed and a bed cover correctly adhering to it; and Compare the difference between the first appearance image and the internal image to generate an initial image. The detection method according to claim 4, wherein the step of obtaining the mask image group further comprises: Remove the bed cover of the carrier bed; Capture a second appearance image of the carrier bed without the bed cover; and Compare the difference between the second appearance image and the initial image to generate an uncovered mask image in the mask image group. The detection method according to claim 4, wherein the step of obtaining the mask image group further comprises: adjusting the position of the bed cover on the carrier bed so that the bed cover protrudes from the carrier bed; Capture a third appearance image of the bed cover protruding from the carrier bed; and Compare the difference between the third appearance image and the initial image to generate a convex mask image in the mask image group. The detection method according to claim 4, wherein the step of obtaining the mask image group further comprises: Adjust the position of the bed cover on the carrier bed to maintain a skew angle between the bed cover and the carrier bed; Capturing a fourth appearance image that maintains the skew angle between the bed cover and the carrier bed; and Compare the difference between the fourth appearance image and the initial image to generate a skewed mask image in the mask image group. The detection method according to claim 1, wherein the step of obtaining the mask image group includes: Capture an internal image of the testing machine; Passing the bed through an image capturing area in the testing machine; Capturing and accumulating images in the image capturing area to generate an appearance image; The difference between the appearance image and the internal image is used as an initial image. The inspection method according to claim 8, further comprising: passing the carrier bed and the inspection object through the image capturing area in the inspection machine; and Capture and accumulate the images in the image capture area to generate the detection image. The detection method according to claim 1, wherein the mask image group includes a plurality of mask images, and the at least one threshold setting condition includes a plurality of threshold setting conditions corresponding to the mask images to determine the detection image If the element meets these threshold setting conditions, if none of them meet, the testing machine performs a scan. A detection system, including: A carrier bed for carrying a test object; A testing machine for scanning and testing the carrier bed; An image detection device, which is used to capture a mask image group of the carrier bed when the test object is not placed on the carrier bed; the case where the test object is carried on the carrier bed Next, the image detector is used to capture a detection image of the carrier bed and the test object; and A processor for generating a detection area according to the mask image group, and for calculating a detection pixel corresponding to the detection area of the detection image, the processor is also used for determining whether the detection pixel meets a threshold Set the conditions, if yes, generate an abnormal message to adjust the bed or the test object. The detection system according to claim 11, wherein the processor is used to calculate the number of pixels in the detected pixels to determine whether the number of pixels exceeds a threshold. The detection system as described in claim 11, further comprising: A storage unit electrically connected to the processor and used to store the mask image group; wherein the image detection device is used to capture a part of the detection machine when the carrier bed is in a correct state The internal image, and a first appearance image when the carrier bed and a bed cover are properly closed are captured, so that the processor is used to compare the difference between the first appearance image and the internal image to generate an initial image. The detection system according to claim 13, wherein the mask image group includes an uncovered mask image, a convex mask image, and an oblique mask image; the uncovered mask image corresponds to the carrier bed not having the bed Appearance when covered; the convex cover image corresponds to the appearance when the bed cover protrudes out of the bed; the skewed cover image corresponds to the appearance when the bed cover and the bed are maintained at an oblique angle.

Images