Learning data collection device, learning data collection method, and program

Abstract

[Problem] To provide a training data collection device that facilitates collection of training data. [Solution] This training data collection device comprises: an image acquisition unit for acquiring a first image that was captured by a camera installed on a ship and that includes a harbor or a marine target, and a second image that includes the target and has a higher resolution than the first image; an image recognition unit for estimating the type of target from the second image; and an association unit for associating the type of target estimated from the second image with the first image.

Description

[Technical Field]

[0001] The present invention relates to a learning data collection device, a learning data collection method, and a program. [Background technology]

[0002] There is a need for image recognition technology to identify targets in ports or at sea. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-077202 Summary of the Invention [Problem to be solved by the invention]

[0004] Achieving such image recognition technology requires a large amount of training data, but manually annotating each image requires a huge amount of effort.

[0005] The present invention has been made in view of the above-mentioned problems, and its main object is to provide a learning data collection device, a learning data collection method, and a program that make it easy to collect learning data. [Means for solving the problem]

[0006] In order to solve the above problem, one aspect of the present invention provides a learning data collection device including an image acquisition unit that acquires a first image including a target in a harbor or on the sea, both of which are captured by a camera installed on a ship, and a second image including the target and having a higher resolution than the first image, an image recognition unit that estimates a type of the target from the second image, and an association unit that associates the type of the target estimated from the second image with the first image. This facilitates the collection of learning data.

[0007] In the above aspect, the second image may be an image captured by the camera at a point closer to the target than the point at which the first image was captured, thereby making it possible to associate the type of the target estimated from the second image captured at the point closer to the target with the first image.

[0008] In the above aspect, the second image may be an image captured by enlarging an area including the target in the first image using an optical zoom function of the camera, thereby making it possible to associate the type of target estimated from the second image captured by enlarging it using the optical zoom function with the first image.

[0009] In the above aspect, the second image may be an image of an area including the target in the first image captured by another camera with a higher resolution than the first camera, thereby making it possible to associate the type of target estimated from the second image captured by the other camera with the first image.

[0010] In the above aspect, the image recognition unit may estimate the type of the target from the second image and calculate an accuracy of the estimation, and the associating unit may associate the type of the target whose accuracy is equal to or greater than a threshold with the first image. This makes it possible to associate the type of the target estimated from the second image whose accuracy is equal to or greater than a threshold with the first image.

[0011] In the above aspect, the image recognition unit may estimate the type of the target from the first image and calculate an accuracy of the estimation, and the associating unit may associate the type of the target estimated from the second image with the first image for which the accuracy is less than a threshold. This makes it possible to associate the type of the target estimated from the second image with the first image for which the accuracy of the estimation is less than a threshold.

[0012] In the above aspect, the image recognition unit may estimate the type of the target from the image captured by the camera and calculate an accuracy of the estimation, and an image whose accuracy is equal to or greater than a threshold may be designated as the second image, and an image whose accuracy is less than the threshold may be designated as the first image. This makes it possible to designate an image whose estimation accuracy is less than a threshold as the first image, and an image whose estimation accuracy is equal to or greater than the threshold as the second image.

[0013] In the above aspect, the system may further include a position and attitude acquisition unit that acquires a position and orientation of the ship, and an identification unit that identifies the target detected in the first image and the target detected in the second image based on the position and orientation of the ship at the time the first image was captured, the in-image position of the target detected in the first image, the position and orientation of the ship at the time the second image was captured, and the in-image position of the target detected in the second image. This makes it possible to identify the target detected in the first image and the target detected in the second image.

[0014] In the above aspect, the associating unit may further associate the first image with incidental data that indicates a state of the ship or its surroundings at the time the first image was captured, thereby making it possible to further include incidental data in the learning data.

[0015] Another aspect of the present invention provides a learning data collection method that acquires a first image including a target in a harbor or on the sea, both of which are captured by a camera installed on a ship, and a second image including the target and having a higher resolution than the first image, estimates the type of the target from the second image, and associates the type of the target estimated from the second image with the first image. This facilitates the collection of learning data.

[0016] In the above aspect, a trained model for estimating the type of the target may be retrained using a data set including the type of the target estimated from the first image and the second image, thereby further improving the recognition accuracy of the trained model.

[0017] According to another aspect of the present invention, a program causes a computer to acquire a first image including a target in a harbor or on the sea, both of which are captured by a camera installed on a ship, and a second image including the target and having a higher resolution than the first image, estimate a type of the target from the second image, and associate the type of the target estimated from the second image with the first image. This facilitates the collection of learning data. [Brief explanation of the drawings]

[0018] [Figure 1] FIG. 1 illustrates an example of a data collection system. [Figure 2] FIG. 1 illustrates an example of a data collection device. [Figure 3] FIG. 2 is a diagram showing an example of a first image. [Figure 4] FIG. 10 is a diagram showing an example of a second image. [Figure 5] FIG. 10 is a diagram illustrating an example of an image capturing point. [Figure 6] FIG. 10 is a diagram showing an example of a recognition result of the first image. [Figure 7] FIG. 10 is a diagram showing an example of a recognition result of a second image. [Figure 8] FIG. 10 is a diagram illustrating an example of a temporary storage database. [Figure 9] FIG. 10 is a diagram illustrating an example of a training dataset. [Figure 10] FIG. 10 is a diagram illustrating an example of a data collection method. DETAILED DESCRIPTION OF THE INVENTION

[0019] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0020] Figure 1 is a block diagram showing an example configuration of a data collection system 100. The data collection system 100 is a system that is installed on a ship and collects learning data for machine learning. In the following description, the ship on which the data collection system 100 is installed is referred to as the "own ship."

[0021] The data collection system 100 includes a data collection device 1, a display unit 2, a radar 3, an AIS 4, a camera 5, a GNSS receiver 6, a gyrocompass 7, an ECDIS 8, and a wireless communication unit 9. These devices are connected to a network N such as a LAN, and are capable of network communication with each other.

[0022] The data collection device 1 is a computer including a CPU, RAM, ROM, non-volatile memory, an input / output interface, etc. The CPU of the data collection device 1 executes information processing according to a program loaded from the ROM or non-volatile memory to the RAM.

[0023] The program may be supplied via an information storage medium such as an optical disk or a memory card, or may be supplied via a communication network such as the Internet or a LAN.

[0024] The display unit 2 displays radar images, camera images, electronic nautical charts, etc. The display unit 2 also displays display images generated by the data collecting device 1.

[0025] The display unit 2 is, for example, a display device with a touch sensor, a so-called touch panel. The touch sensor detects a position on the screen pointed to by a user's finger or the like. However, the pointed position may also be input by a trackball or the like.

[0026] The radar 3 emits radio waves around the ship and receives the reflected waves, generating echo data based on the received signals. The radar 3 also identifies targets from the echo data and generates target tracking data (TT data) that indicate the position and speed of the targets.

[0027] The AIS (Automatic Identification System) 4 receives AIS data from other ships around the ship or from land-based control. Instead of AIS, a VDES (VHF Data Exchange System) may also be used. The AIS data includes the identification codes, names, positions, courses, speeds, types, lengths, and destinations of other ships.

[0028] The camera 5 is a digital camera that captures images of the outside from the ship and generates image data. The camera 5 is installed, for example, on the bridge of the ship, facing the bow direction. The camera 5 may be a camera with pan / tilt and optical zoom functions, a so-called PTZ camera.

[0029] The camera 5 is preferably a wide-angle camera, for example. The camera 5 is not limited to a visible light camera, and may be an infrared camera.

[0030] The GNSS receiver 6 detects the ship's position based on radio waves received from the GNSS (Global Navigation Satellite System). The gyrocompass 7 detects the ship's heading. A GPS compass may be used instead of a gyrocompass.

[0031] An ECDIS (Electronic Chart Display and Information System) 8 acquires the ship's position from the GNSS receiver 6 and displays the ship's position on an electronic chart. The ECDIS 8 also displays the ship's planned route on the electronic chart. A GNSS plotter may be used instead of an ECDIS.

[0032] The wireless communication unit 9 includes various types of wireless equipment, such as wireless equipment for the very short wave band, the medium short wave band, and the short wave band, for realizing communication with other ships or land-based control.

[0033] In this embodiment, the data collection device 1 and the display unit 2 are independent devices, but this is not limiting, and the data collection device 1 and the display unit 2 may be an integrated device.

[0034] In this embodiment, the data collection device 1 is an independent device, but is not limited to this and may be integrated with another device such as the ECDIS 8. In other words, the functional units of the data collection device 1 may be realized by another device.

[0035] Furthermore, the display unit 2 is also an independent device, but is not limited to this. A display unit of another device such as ECDIS 8 may be used as the display unit 2 that displays the display image generated by the data collection device 1.

[0036] 2 is a block diagram showing an example configuration of the data collection device 1. The data collection device 1 includes an image acquisition unit 11, a position and orientation acquisition unit 12, an image recognition unit 13, an identification unit 14, an association unit 15, a model holding unit 16, a temporary storage unit 17, and a data storage unit 18.

[0037] The functional units 11 to 15 included in the data collection device 1 are realized by the control unit 10 (processing circuitry) of the data collection device 1 executing information processing according to a program. The storage units 16 to 18 included in the data collection device 1 are secured in the memory of the data collection device 1.

[0038] The image acquisition unit 11 acquires images captured by the camera 5. The image acquisition unit 11 sequentially acquires a plurality of time-series images from the camera 5 and sequentially provides them to the image recognition unit 13. The time-series images are, for example, a plurality of still images (frames) included in moving image data.

[0039] The images acquired by the image acquisition unit 11 are images taken by the camera 5 when the ship is navigating the waters of a port or the like, and are images that include targets in the port or at sea. Targets in the port are, for example, installations for handling cargo, such as cranes, or installations for mooring, such as quays. Targets at sea are, for example, ships, buoys, etc.

[0040] Specifically, the image acquisition unit 11 acquires a first image P1 including a target in a harbor or on the sea, and a second image P2 including the installation and having a higher resolution than the first image P1. In this embodiment, the second image P2 is an image captured by the camera 5 at a point closer to the target than the point at which the first image P1 was captured.

[0041] Figures 3 and 4 are diagrams showing examples of the first image P1 and the second image P2. Figure 5 is a diagram showing examples of image capture locations. K1 and D1 represent the position and orientation of the ship SS at the time the first image P1 was captured, and K2 and D2 represent the position and orientation of the ship SS at the time the second image P2 was captured.

[0042] In the example shown in Fig. 3, three cranes C1 to C3 are included in the center of the first image P1 and are relatively small. In contrast, in the example shown in Fig. 4, two cranes C1 and C2 are included in the entire second image P2 and are relatively large. Note that in the example shown in Fig. 4, crane C3 is outside the angle of view of camera 5 and is not included in the second image P2.

[0043] In this way, the second image P2 has a higher resolution than the first image P1, making it easier to identify the cranes C1 and C2. This is because, as shown in the example of Figure 5, the first image P1 was captured at a position K1 that was relatively far from the quay PQ where the cranes C1 to C3 were installed, while the second image P2 was captured at a position K2 that was relatively close to the quay PQ.

[0044] In other words, the first image P1 is an image in which targets can be detected but not classified by the image recognition unit 13 at a later stage. On the other hand, the second image P2 is an image in which targets can be detected and classified by the image recognition unit 13. Being able to classify means, for example, that the accuracy of estimating the type is at a sufficient level.

[0045] Therefore, whether the image is the first image P1 or the second image P2 may be determined by the subsequent image recognition unit 13. That is, as a result of classification by the image recognition unit 13, if the accuracy of estimation is less than a threshold, the image may be determined as the first image P1, and if the accuracy of estimation is equal to or greater than the threshold, the image may be determined as the second image P2.

[0046] Without being limited to this, the second image P2 may be, for example, an image captured by enlarging the range of the cranes C1 and C2 detected in the first image P1 using the optical zoom function of the camera 5, or an image captured by another camera with higher resolution than the camera 5.

[0047] The second image P2 may be, for example, a high-resolution image of the area of ​​the cranes C1 and C2 detected in the first image P1. Alternatively, the first image P1 may be an infrared image captured by an infrared camera, and the second image P2 may be a visible light image captured by a visible light camera.

[0048] 2 acquires the position and orientation of the ship at the time the image was captured by the camera 5, and associates this with the image acquired by the image acquisition unit 11. The position of the ship is the position of the ship detected by the GNSS receiver 6.

[0049] The heading of the ship is the heading of the ship detected by the gyrocompass 7. The heading of the ship may also include not only the heading of the ship but also the roll or pitch of the ship detected by an attitude sensor (not shown).

[0050] The image recognition unit 13 uses the trained model stored in the model storage unit 16 to estimate the type of a target included in an image provided from the image acquisition unit 11. Specifically, the image recognition unit 13 detects the position of the target included in the image within the image, estimates the type of the target, and calculates the accuracy of the estimation.

[0051] The trained model is generated in advance by machine learning using training images as input data and the in-image positions and types of targets included in the training images as training data. The trained model generated in this way outputs the in-image positions of targets included in the images, the target types, and the accuracy of estimation.

[0052] The trained model may be, for example, an object detection model such as SSD (Single Shot MultiBox Detector) or YOLO (You Only Look Once). Alternatively, the trained model may be a segmentation model such as Semantic Segmentation or Instance Segmentation.

[0053] 6 and 7 are diagrams showing examples of the recognition results of the first image P1 and the second image P2 by the image recognition unit 13. FIG.

[0054] Cranes C1 to C3 included in the first image P1 are surrounded by bounding boxes B1 to B3, and labels L1 to L3 describing the estimated type and its accuracy are added to the bounding boxes B1 to B3.

[0055] Similarly, the cranes C1 and C2 included in the second image P2 are surrounded by bounding boxes B1 and B2, and labels L1 and L2 describing the estimated type and its accuracy are added to the bounding boxes B1 and B2.

[0056] The first image P1 has a relatively low resolution, and therefore the accuracy of the estimation is relatively low. In the example of Figure 6, the estimated types of cranes C1 and C3 are incorrect and the accuracy of the estimation is low. The estimated type of crane C2 is correct, but the accuracy of the estimation is low.

[0057] On the other hand, the second image P2 has a relatively high resolution, and therefore the accuracy of the estimation is relatively high. In the example of Fig. 7, the estimated types of the cranes C1 and C2 are correct and the accuracy of the estimation is also high.

[0058] 2 temporarily stores a first image P1. In this embodiment, among the images acquired by the image acquisition unit 11, an image including a target whose estimation accuracy is less than a threshold as a result of recognition by the image recognition unit 13 is stored in the temporary storage unit 17 as the first image P1.

[0059] 8 is a diagram showing an example of a temporary storage database for managing the first image P1 stored in the temporary storage unit 17. The temporary storage database includes fields such as "target ID," "image," "position in image," "type," "accuracy," "image capture position," "image capture direction," "estimated position," and "accompanying data."

[0060] "Target ID" is an identifier for identifying a target. "Image" represents the file name of the first image P1. In this example, target IDs 001 to 003 correspond to cranes C1 to C3 shown in the example of FIG. 6.

[0061] The "in-image position" represents the in-image position of the target detected in the first image P1. The in-image position is expressed, for example, by the coordinates of the upper left corner point and the lower right corner point of the bounding box surrounding the target.

[0062] "Type" indicates the type of target estimated by the image recognition unit 13. In this example, the type of target ID: 001,003 is mistakenly estimated as a radio tower or a bridge, rather than a crane. "Accuracy" indicates the accuracy of the estimation. Accuracy is expressed, for example, as a value between 0 and 1, with the closer to 1 the accuracy is, the higher the accuracy.

[0063] "Imaging position" indicates the position of the ship at the time the first image P1 was captured. The ship's position is expressed, for example, by latitude and longitude. "Imaging direction" indicates the imaging direction of the camera 5 at the time the first image P1 was captured. The imaging direction of the camera 5 corresponds to the bow direction of the ship.

[0064] The "estimated position" represents the estimated position of the target calculated based on the position in the image, the image capturing position, the image capturing direction, etc. In addition, parameters such as the angle of view and resolution of the camera 5 are also used to calculate the estimated position.

[0065] Specifically, the estimated position is first calculated as a relative position to the ship from the position in the image and the imaging direction, and then converted to an absolute position using the imaging position. The estimated position is expressed, for example, by latitude and longitude, just like the imaging position.

[0066] The "auxiliary data" is data that represents the state of the ship or its surroundings at the time the first image P1 was captured. The auxiliary data includes, for example, data on weather conditions such as fog or rain, or area attributes such as a port or perspective.

[0067] 2 identifies targets detected in the first image P1 and targets detected in the second image P2. In this embodiment, among the images acquired by the image acquisition unit 11, an image in which the estimation accuracy of all detected targets is equal to or greater than a threshold as a result of recognition by the image recognition unit 13 is provided to the identification unit 14 as the second image P2.

[0068] When the identification unit 14 receives the second image P2, it extracts, from the first images P1 stored in the temporary storage unit 17, the first images P1 that include the same target as the target detected in the second image P2.

[0069] The identification unit 14 determines that the two targets are the same when the estimated position of the target detected in the first image P1 and the estimated position of the target detected in the second image P2 are the same or similar. As described above, the estimated position of the target is calculated based on the position in the image, the image capture position, the image capture orientation, etc.

[0070] The associating unit 15 associates the first image P1 with the target type estimated from the second image P2 with an estimation accuracy equal to or greater than a threshold value. Then, the associating unit 15 stores a data set including the first image P1 and the target type associated therewith in the data storage unit 18 as a learning data set for machine learning.

[0071] Specifically, the associating unit 15 associates the type of target estimated from the second image P2 by the image recognition unit 13 with the first image P1 that includes the same target as in the second image P2 and that has been extracted from the temporary storage unit 17 by the identification unit 14. The associating unit 15 may further associate incidental data with the first image P1.

[0072] 9 is a diagram showing an example of a learning dataset stored in the data storage unit 18. The learning dataset includes a first image P1, the in-image positions and types of targets included in the first image P1, and accompanying data.

[0073] Among these, the target type is the type of the target estimated from the second image P2. That is, the target type estimated from the first image P1 with an estimation accuracy below the threshold is replaced with the target type estimated from the second image P2 with an estimation accuracy equal to or greater than the threshold.

[0074] This makes it possible to associate the first image P1, in which the target cannot be classified, with a highly reliable type estimated from the second image P2, thereby making it possible to obtain a suitable learning dataset.

[0075] In the example of Figure 8 above, the type of some of the targets in image IMG01 was mistakenly estimated to be a radio tower or a bridge rather than a crane, but in the example of Figure 9, the type of targets estimated from the second image P2 was applied, and as a result, the type of all of the targets in image IMG01 was determined to be a crane.

[0076] Returning to the explanation of Fig. 2, the learning dataset stored in the data storage unit 19 is used for re-learning of a trained model by the learning device 200. The learning device 200 is, for example, one or more server computers installed on land.

[0077] When communication with the learning device 200 is established, for example, when the data collection device 1 calls at port, the data collection device 1 transfers the learning dataset stored in the data storage unit 18 to the learning device 200. In addition, the data collection device 1 deletes the learning dataset that has been transferred from the data storage unit 18 to create space.

[0078] The learning device 200 performs re-learning of the trained model using the training dataset acquired from the data collection device 1. Specifically, the learning device 200 performs re-learning using the images included in the acquired training dataset as input data and the position, type, and accompanying data of the target in the image as training data.

[0079] When a new version of the trained model is prepared in the learning device 200, the data collection device 1 acquires the new version of the trained model from the learning device 200 and replaces the old version of the trained model stored in the model storage unit 16.

[0080] In this way, by repeating the cycle of retraining the trained model using the training dataset collected by the data collection device 1 and then collecting training datasets using the retrained trained model by the data collection device 1, continuous improvement of the trained model is possible.

[0081] 10 is a diagram showing an example of the procedure of a data collection method realized in the data collection device 1. The CPU of the data collection device 1 executes the information processing shown in the diagram in accordance with a program.

[0082] First, the data collecting device 1 acquires an image captured by the camera 5 (S11, processing as the image acquiring unit 11). Next, the data collecting device 1 acquires the position and attitude of the ship itself and associates them with the image (S12-S13, processing as the position and attitude acquiring unit 12).

[0083] Next, the data collection device 1 performs image recognition (S14, processing as the image recognition unit 13). Specifically, the data collection device 1 uses the trained model to detect the position of a target included in the image, estimate the type of the target, and calculate the accuracy of the estimation.

[0084] Next, the data collection device 1 designates the image containing the target whose estimation accuracy is less than the threshold as the first image P1 (S15: YES), registers the first image P1 and information related to the target in a temporary storage database (see Figure 8), and terminates the processing.

[0085] On the other hand, the data collection device 1 designates the image in which the estimation accuracy of all targets is above the threshold as the second image (S15: NO), and determines whether or not there is a first image P1 that includes targets identical to those in the second image P2 by referring to the temporary storage database (S17, S18, processing as the identification unit 14).

[0086] If a first image P1 exists that includes the same target as the second image P2 (S18: YES), the data collection device 1 associates the type of target estimated from the second image P2, whose estimation accuracy is above a threshold, with the first image P1 and saves it (S19, processing as the association unit 15), and terminates the processing.

[0087] Although the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and it goes without saying that various modifications can be made by those skilled in the art.

[0088] For example, a camera other than camera 5 may be installed on the ship, and the image captured by the other camera may be designated as first image P1, the image captured by camera 5 may be designated as second image P2, and the classification result of second image P2 may be associated with first image P1. This makes it possible to collect a learning dataset for the other camera. In this case, it is preferable to correct the position within the image according to the parallax based on the differences in position, resolution, and angle of view between camera 5 and the other camera. [Explanation of symbols]

[0089] 1 Data collection device, 2 Display unit, 3 Radar, 4 AIS, 5 Camera, 6 GNSS receiver, 7 Gyrocompass, 8 ECDIS, 9 Wireless communication unit, 11 Image acquisition unit, 12 Position and attitude acquisition unit, 13 Image recognition unit, 14 Identification unit, 15 Association unit, 16 Model holding unit, 17 Temporary storage unit, 18 Data storage unit, 100 Data collection system, 200 Learning device

Claims

1. an image acquisition unit that acquires a first image including a target in a harbor or on the sea, both of which are captured by a camera installed on the ship, and a second image including the target and having a higher resolution than the first image; an image recognition unit that estimates the type of the target from the second image; an associating unit that associates the type of the target estimated from the second image with the first image; A learning data collection device comprising:

2. The second image is an image captured by the camera at a point closer to the target than the point at which the first image was captured. The learning data collection device according to claim 1 .

3. The second image is an image captured by enlarging an area including the target in the first image using an optical zoom function of the camera. The learning data collection device according to claim 1 .

4. The second image is an image of a range including the target in the first image captured by another camera having a higher resolution than the camera. The learning data collection device according to claim 1 .

5. the image recognition unit estimates the type of the target from the second image and calculates the accuracy of the estimation; The associating unit associates the type of the target whose certainty is equal to or greater than a threshold with the first image.

5. The learning data collection device according to claim 1.

6. the image recognition unit estimates the type of the target from the first image and calculates an accuracy of the estimation; the associating unit associates the type of the target estimated from the second image with the first image for which the accuracy is less than a threshold value; 6. The learning data collection device according to claim 1.

7. the image recognition unit estimates the type of the target from the image captured by the camera and calculates an accuracy of the estimation, and determines an image whose accuracy is equal to or greater than a threshold as the second image, and determines an image whose accuracy is less than the threshold as the first image; 7. The learning data collection device according to claim 1.

8. a position and attitude acquisition unit that acquires the position and orientation of the ship; an identification unit that identifies the target detected in the first image and the target detected in the second image based on the position and orientation of the ship at the time the first image was captured, the position within the image of the target detected in the first image, the position and orientation of the ship at the time the second image was captured, and the position within the image of the target detected in the second image; Further provided with 8. The learning data collection device according to claim 1.

9. The associating unit further associates, with the first image, incidental data that represents a state of the ship or its surroundings at the time the first image was captured.

9. The learning data collection device according to claim 1.

10. acquiring a first image including a target in a port or on the sea, both of which are captured by a camera installed on a ship, and a second image including the target and having a higher resolution than the first image; Estimating the type of the target from the second image; Associating the type of the target estimated from the second image with the first image; Methods for collecting data for training.

11. further retraining a trained model for estimating the type of the target using a dataset including the type of the target estimated from the first image and the second image; The learning data collection method according to claim 10.

12. acquiring a first image including a target object at a port or on the sea, both of which are captured by a camera installed on a ship, and a second image including the target object and having a higher resolution than the first image; Estimating a type of the target from the second image; and Associating the type of the target estimated from the second image with the first image; A program that causes a computer to execute the following.

Images