JP6341550B1 - Construction site image determination device and construction site image determination program - Google Patents

Abstract

An object of the present invention is to make it easy to grasp the ability of a field worker who performs construction work on a construction site. In a construction site image DB, a plurality of learning construction site images and good / bad determinations regarding each learning construction site image are stored in association with each other. The learning processing unit 40 learns the learning device 36 by using a plurality of learning construction site images and learning data including good / bad determination regarding each learning construction site image. The determination unit 42 inputs a determination target construction site image to the learned learner 36. The determination unit 42 performs good / bad determination regarding the determination target construction site image based on the output data of the learning device 36, and is in charge of the construction corresponding to the determination target construction site image associated with the determination target construction site image. Based on the worker identification information indicating the on-site worker, the quality judgment regarding the construction site image to be judged is distinguished and output for each on-site worker. Thereby, the good / bad determination regarding several determination object construction site images is output for every field worker. [Selection] Figure 2

Description

  The present invention relates to a construction site image determination apparatus and a construction site image determination program.

  Conventionally, on a construction site, a construction site image showing a construction result has been taken for the purpose of recording the progress of the construction or managing the progress of the construction. The construction site image is submitted to a site supervision of the construction site or a construction orderer (hereinafter simply referred to as “manager”), for example, attached to a daily report. The manager determines whether the construction result is good or bad based on the submitted construction site image.

  Since there may be many construction site images submitted to the manager, a technique for managing the construction site images has been proposed. For example, Patent Document 1 discloses a construction site management system that can easily manage a large number of construction site images.

  On the other hand, machine learning techniques such as deep learning have been proposed. For example, in Patent Document 2, a learning device is learned using a learning image obtained by photographing a building element as learning data, and a building element included in a newly photographed photographed image is obtained using a learned learning device. A technique for identifying is disclosed.

Japanese Unexamined Patent Publication No. 2011-175584 JP 2017-107443 A

  By the way, there is a demand for managers to grasp the ability of field workers who perform construction work at construction sites. The field worker's ability here means in particular the ability to properly complete the construction.

  However, there is a fact that it is difficult for managers to grasp the capabilities of field workers. There are various reasons for this, but one reason is that it is common for managers to be in charge of multiple construction sites. It is mentioned that the state of construction of the worker cannot be fully confirmed.

  Generally, each time a construction process is completed, a construction site image indicating a construction result is taken, and the construction site image is submitted to a manager. Therefore, it is conceivable that the manager grasps the ability of the site worker by analyzing the quality judgment of the plurality of construction site images. However, in reality, an enormous number of construction site images indicating the construction results of a large number of site workers are submitted to the manager, and the manager visually checks each of the construction site images one by one. Therefore, due to time constraints, etc., the manager can only determine the quality of each large number of construction site images individually, and it is virtually impossible to understand the capabilities of site workers by analyzing multiple construction site images. It has become difficult.

  An object of the present invention is to make it easy to grasp the ability of a field worker who performs construction work at a construction site.

  The present invention learns using a plurality of learning construction site images indicating construction results and learning data including good / bad judgments on the construction results indicated by the respective learning construction site images, so that A learning device capable of determining a defect, and a determination target construction site image is input to the learning device, and a good / bad determination regarding the determination target construction site image is performed based on output data of the learning device, and the determination target construction site image Based on the worker identification information indicating the site worker in charge of the work corresponding to the judgment target construction site image, the quality judgment regarding the judgment target construction site image is distinguished and output for each site worker. And a determination unit that outputs a determination result including a good / bad determination regarding a plurality of determination target construction site images for each site worker. It is.

  Preferably, the learning device further learns using the learning data including the type of defect specified for the learning construction site image determined to be defective, so that the quality determination and the type of failure regarding the construction site image are performed. When the determination unit determines a defect for the determination target construction site image, the determination unit further determines the type of defect related to the determination target construction site image based on the output data of the learning device, It is characterized in that a determination result including a good / bad determination and a defect type regarding a plurality of determination target construction site images is output for each site worker.

  Preferably, a plurality of learning devices are provided corresponding to a plurality of construction steps, and each learning device learns using a plurality of the learning data including the learning construction site image of the step corresponding to itself. The determination unit inputs the determination target construction site image to the learning device selected based on the process identification information indicating the construction process indicated by the determination target construction site image, which is associated with the determination target construction site image. Thus, the determination result relating to the determination target construction site image is output separately for each site worker and for each process, and the determination regarding the plurality of determination target construction site images for each set of the site worker and the process. The result is output.

  In addition, the present invention learns a computer by using a plurality of learning construction site images indicating construction results, and learning data including good / bad judgment for the construction results indicated by each learning construction site image, A learning device capable of determining good and bad regarding the construction site image, and inputting the determination target construction site image to the learning device, performing the good and defective determination regarding the determination target construction site image based on the output data of the learning device, Based on the worker identification information indicating the site worker in charge of the construction corresponding to the judgment target construction site image associated with the judgment target construction site image, the site worker performs the good / bad judgment regarding the judgment target construction site image. A distinction is output for each site, and a function is made to function as a determination unit that outputs a determination result including a good / bad determination regarding a plurality of determination target construction site images for each site worker. Is a construction site image determination program which is characterized.

  According to the present invention, it is possible to easily grasp the tendency of good / bad for each field worker regarding the construction result.

1 is a schematic configuration diagram of a construction site image determination system according to the present embodiment. 1 is a schematic configuration diagram of a server according to the present embodiment. It is a conceptual diagram which shows the content of construction site image DB. It is a figure which shows the structure outline of the learning device using a neural network. It is a conceptual diagram which shows a mode that the determination result of a learning device is distinguished and output for every field worker. It is a conceptual diagram which shows a mode that the determination result of a learning device is distinguished and output for every field worker and every process.

  Hereinafter, embodiments of the present invention will be described.

  FIG. 1 is a schematic configuration diagram of a construction site image determination system 10 according to the present embodiment. The construction site image determination system 10 includes a digital camera 12, an administrator terminal 14, and a server 16 as a construction site image determination device. The digital camera 12, the administrator terminal 14, and the server 16 are communicably connected to each other via a communication line 20 constituted by a LAN (Local Area Network) or the Internet so that they can communicate with each other. In FIG. 1, one digital camera 12 and one administrator terminal 14 are shown, but a plurality of them may be provided.

  The digital camera 12 may be a literal digital camera or a tablet terminal (mobile terminal) equipped with a camera function. The digital camera 12 is used by a person who performs work on a construction site. The construction site in the present embodiment is, for example, a construction site or a renovation site. In the present embodiment, it is assumed that a site worker who performs construction on a construction site uses the digital camera 12. The digital camera 12 is for taking a construction site image showing a construction result (performance) at the construction site. In the present embodiment, every time a construction process is completed, the site worker photographs a construction site image indicating the construction result of the process.

  The construction site image is associated with metadata as supplementary information. The metadata includes worker identification information indicating a site worker who is in charge of the construction corresponding to the construction site image. Further, the metadata may include process identification information indicating a construction process indicated by the construction site image. Further, the metadata may include construction site identification information for identifying the construction site or shooting time information.

  The digital camera 12 has a communication function, and the photographed construction site image and metadata are transmitted to the server 16.

  The administrator terminal 14 is a terminal used by the administrator. The manager terminal 14 is installed in a place remote from the construction site. The administrator terminal 14 may be a general computer and includes a control unit including a CPU (Central Processing Unit), a storage unit including a hard disk, a ROM (Read Only Memory) or a RAM (Random Access Memory), a network adapter, and the like. And a communication unit including a display unit including a liquid crystal panel and an input unit including a keyboard or a mouse.

  FIG. 2 is a schematic configuration diagram of the server 16. The server 16 is configured by a computer having performance that can function as a server. In the present specification, the construction site image included in the learning data for learning of the learning device 36 (described later) of the server 16 is referred to as “learning construction site image” and the quality of the learning by the learned learning device 36. The construction site image to be judged is described separately from the “construction site image to be judged”. In particular, if the two are not distinguished, they are simply described as “construction site image”.

  The communication unit 30 includes, for example, a network adapter. The communication unit 30 exhibits a function of communicating with other devices via the communication line 18. For example, the communication unit 30 receives a construction site image with metadata from the digital camera 12. In addition, the communication unit 30 transmits a determination result indicating good / bad determination regarding the determination target construction site image to the administrator terminal 14.

  The storage unit 32 includes, for example, a hard disk, ROM, or RAM. The storage unit 32 stores a construction site image determination program for operating each unit of the server 16. In addition, a construction site image DB 34 is defined in the storage unit 32.

  FIG. 3 is a conceptual diagram showing the contents of the construction site image DB 34. In the construction site image DB 34, for example, a plurality of learning construction site images, which are construction site images photographed by each site worker in the past at each construction site, and meta data relating to each learning construction site image. Data is stored and stored. As shown in FIG. 3, in the present embodiment, the construction site image for learning, the worker identification information indicating the site worker in charge of the construction corresponding to the construction site image for learning, and the construction site are supported. A plurality of process identification information indicating processes is associated and stored.

  The construction site image for learning acquired by the digital camera 12 is judged good or bad by the administrator. The learning construction site image is acquired by the digital camera 12 and then transmitted to the manager terminal 14 via the server 16. The administrator confirms the learning construction site image displayed on the display unit of the administrator terminal 14 to determine whether the learning construction site image is good or bad. In other words, if the construction result indicated by the learning construction site image is appropriate, the judgment is good, and if not, the judgment is bad. The administrator uses the input unit of the administrator terminal 14 to input the quality determination regarding the learning construction site image, and the input quality determination is transmitted from the administrator terminal 14 to the server 16. In the construction site image DB 34, the quality determination received from the manager terminal 14 is stored in association with the learning construction site image.

  Returning to FIG. 2, the learning unit 36 is stored in the storage unit 32. The actual state of the learning device 36 is a processing execution program for processing each parameter (such as weight and bias) related to each unit (neuron) and input data. Therefore, storing the learning device 36 in the storage unit 32 means that various parameters related to the learning device 36 and a process execution program are stored in the storage unit 32.

  The learning device 36 according to the present embodiment learns using a plurality of learning construction site images stored in the construction site image DB 34 and learning data including good / bad judgments regarding each learning construction site image. Then, for example, a determination target construction site image, which is a newly acquired construction site image, is input to the learning device 36 as input data, and the learning device 36 performs good / bad determination regarding the determination target construction site image. That is, when the construction result indicated by the judgment target construction site image is appropriate, the judgment is good, and when the construction result indicated by the judgment target construction site image is not appropriate, the judgment is bad.

  The learning device 36 may be any learning device as long as the learning is possible as described above and the quality determination regarding the determination target construction site image is possible. Details of the structure of the learning device 36 will be described later.

  The control unit 38 includes, for example, a CPU. The control unit 38 controls each unit of the server 16 according to the construction site image determination program stored in the storage unit 32. The control unit 38 also functions as a learning processing unit 40 and a determination unit 42.

  The learning processing unit 40 executes the learning process of the learning device 36. Specifically, the learning construction site image is input to the learning device 36, and the learning device 36 is learned so that the output data of the learning device 36 matches the good / bad determination regarding the learning construction site image.

  The determination unit 42 inputs the determination target construction site image to the learned learner 36, and performs quality determination on the determination target construction site image based on the output data of the learning unit 36. In particular, the determination unit 42 according to the present embodiment performs good / bad determination regarding the determination target construction site image for each site worker based on the worker identification information associated with the determination target construction site image input to the learning device 36. Discriminately output. The determination unit 42 repeats this process, and outputs a determination result including good / bad determination regarding a plurality of determination target construction site images for each site worker.

  The determination result output by the determination unit 42 is transmitted to the administrator terminal 14 in response to a request from the administrator terminal 14, for example. Thereby, the determination result is provided to the administrator.

Hereinafter, a specific example of processing of the learning processing unit 40 and the determination unit 42 will be described together with details of the learning device 36. In this embodiment, the learning device 36 uses a neural network. FIG. 4 is a diagram showing the structure of the learning device 36 in the present embodiment. The input data of the learning device 36 includes a plurality of input variables x ₁ , x ₂ , x ₃ . The input variable of each input data is a pixel value of each pixel of the construction site image.

  The learning device 36 includes a plurality of layers each including a plurality of units 50. Usually, it is configured to include an input layer positioned closest to the input side, an output layer positioned closest to the output side, and an intermediate layer (also referred to as a hidden layer) provided between the input layer and the output layer. In the example of FIG. 4, the intermediate layer is a single layer, but the intermediate layer may have a plurality of layers.

で表される。ここで、Ｉは入力変数の数である。なお、ユニット５０毎に、重みｗ_１、ｗ_２、ｗ_３・・・と、バイアスｂはそれぞれ異なるものであってよい。 Each input variable is input to each unit 50 of the input layer. In each unit 50, weights w ₁ , w ₂ , w ₃ ... And a bias b are defined for each input variable. A value obtained by adding a bias to the sum of values obtained by multiplying the weights corresponding to the respective input variables becomes the input of the unit 50. That is, the input u for one unit 50 in the input layer is

It is represented by Here, I is the number of input variables. Note that the weights w ₁ , w ₂ , w ₃ ... And the bias b may be different for each unit 50.

Each unit 50 outputs an output of a function f called an activation function for the input u. That is, the output z of each unit 50 is
z = f (u) (Formula 2)
It is represented by As the activation function, for example, a sigmoid function, a ramp function, or a step function can be used. The output from each unit 50 in the input layer is input to each unit 50 in the intermediate layer. That is, each unit 50 in the input layer and each unit 50 in the intermediate layer are fully coupled.

  Each unit 50 in the intermediate layer performs the same processing as described above using the output of each unit 50 in the input layer as an input. That is, the weight and bias for each unit 50 in the input layer are set in each unit 50 in the intermediate layer. The output from each unit 50 in the intermediate layer is input to each unit 50 in the output layer. That is, each unit 50 of the intermediate layer and each unit 50 of the output layer are also fully coupled. Each unit 50 in the output layer also performs the same processing as described above with the output of each unit 50 in the intermediate layer as an input. That is, the weight and bias for each unit 50 in the intermediate layer are set for each unit 50 in the output layer.

  The output of each unit 50 in the output layer is input to a softmax function. The softmax function is a function that calculates a plurality of output variables based on the output of each unit 50 in the output layer. The plurality of output variables become the output data of the learning device 36. Each output variable corresponds to each classification label (category) on which the learning device 36 performs classification. As will be described later, the value of each output variable indicates the probability that the input data belongs to each classification label. Become.

ここで、ｙ_ｋはｋ番目の出力変数であり、Ｍは出力層のユニット数であり、ｚ_ｊ（ｋ）はｊ（ｋ）番目の出力層のユニット５０の出力値である。式（３）が示す通り、ソフトマックス関数により、出力層のｋ番目のユニット５０の出力がｙ_ｋに変換される。すなわち、ソフトマックス関数により、出力層の各ユニット５０の出力が各出力変数ｙに変換されている。 As shown in FIG. 4, if the output of the softmax function has a plurality of output variables y ₁ , y ₂ , y ₃ ..., The output variables y ₁ , y ₂ , y ₃ . It represents each probability belonging to each classification label of the input data. Specifically, the softmax function is given by the following equation.

Here, y _k is the k-th output variable, M is the number of units in the output layer, and z _{j (k)} is the output value of the unit 50 in the j (k) -th output layer. As shown in equation (3), the output of the kth unit 50 in the output layer is converted to y _k by the softmax function. That is, the output of each unit 50 in the output layer is converted into each output variable y by the softmax function.

A feature of the softmax function is that the total of each output is 1. Therefore, if the output variable y _k corresponds to the classification label k, the value of the output variable y _k can be interpreted as representing the probability that the input data belongs to the classification label k.

In the simplest possible configuration of the learning device 36 that performs good / bad determination regarding the construction site image to be determined, there are two output variables (classification labels), and the first output variable y ₁ is good determination, second It becomes the output variable y ₂ of corresponding to the determined defective. In this case, the learning processing unit 40 inputs a learning construction site image with a known good / bad determination as learning data to the learning device 36, and the output data for the learning construction site image relates to the learning construction site image. Each weight and bias of each unit 50 in each layer is adjusted so that the value of the output variable corresponding to pass / fail judgment increases and the value of the other output variables decreases (that is, the learning device 36 is learned). For example, when the learning construction site image is a good determination, the learning processing unit 40 increases the value of the output variable y _{1 and} the value of the output variable y _{2 in} the output data for the learning construction site image. Each weight and bias of each unit 50 of each layer is adjusted so as to be smaller.

The determination unit 42 inputs the determination target construction site image as input data to the learning device 36 that has been sufficiently learned. And based on the value of each output variable contained in the output data of the learning device 36, the quality determination regarding the said judgment object construction site image is performed. For example, if the output variable y ₁ corresponding to the good determination shows a larger value than the output variable y ₂ corresponding to the defective determination, the determination unit 42 sets the determination target construction site image as a good determination. On the other hand, the output variable y ₂ is if a larger value than the output variables y _1, to the determination target construction site image and defective judgment.

  As described above, the determination unit 42 distinguishes and outputs the determination result related to the determination target construction site image for each site worker. FIG. 5 shows a state in which the determination result related to the determination target construction site image is output separately for each site worker. As illustrated in FIG. 5, the determination unit 42 inputs the determination target construction site image to the learning device 36, and based on the worker identification information associated with the determination target construction site image, the determination result of the learning device 36. Are output separately for each field worker. When the determination unit 42 repeats this process, determination results regarding a plurality of determination target construction site images are output for each site worker.

  The determination result of the determination unit 42 is transmitted to the administrator terminal 14. Based on the received determination result, the manager terminal 14 distinguishes the quality determinations regarding the plurality of determination target construction site images for each site worker and displays them on the display unit. It can be said that the good / bad determination regarding the plurality of determination target construction site images is statistical data indicating whether or not the construction result is appropriate. Therefore, it can be said that the display unit of the manager terminal 14 displays statistical data indicating whether the construction result is appropriate or not for each site worker.

  The manager can grasp the ability of each site worker based on statistical data indicating whether or not the construction result distinguished for each site worker is appropriate. For example, if the number of defect judgments of the field worker B is significantly greater than the field worker A, it can be said that the ability of the field worker A is higher than that of the field worker B.

  Moreover, based on the shooting time information of the plurality of determination target construction site images, the good / bad determination may be displayed side by side in the order of the shooting times of the determination target construction site images. Thereby, the administrator is provided with statistical data in which whether or not the construction result is appropriate is arranged in time series. According to such statistical data, the manager can grasp the temporal change of whether or not the construction result is appropriate for each field worker. For example, the site worker A can easily grasp a change in which there were many defects before, but there are few defects recently. In this case, it can also be seen that the ability of the field worker A has improved as a result of some device or effort.

  As described above, according to the construction site image determination system 10 according to the present embodiment, the quality determination regarding the plurality of determination target construction site images is output for each site worker. The ability of the operator can be grasped. Further, in the construction site image determination system 10, since the learning device 36 is used to perform the quality determination regarding the determination target construction site image, it is not necessary for the administrator to perform the quality determination of the determination target construction site image, and the accuracy is high. Judgment is realized. As a result, the manager can easily grasp the capabilities of each field worker.

Returning to FIG. 4, the learning device 36 may output output data having three or more output variables (y ₁ , y ₂ , y ₃ ...). In this case, the first output variable y ₁ corresponds to the good determination, and the second and subsequent output variables y ₂ , y ₃ ... Correspond to the defective determination. A plurality of output variables y ₂ , y ₃ ... Corresponding to the defect determination correspond to different types of defects. The type of defect is classified according to, for example, a defect location or a defect mode. In this way, by outputting output data having three or more output variables, the learning device 36 can determine not only the quality of defects but also the type of failure with respect to the determination target construction site image. It becomes.

When the output data of the learning device 36 has three or more output variables, in the construction site image DB 34, information indicating the type of failure specified by the administrator is associated with the learning site image for failure determination. It is done. The learning processing unit 40 stores the plurality of learning construction site images stored in the construction site image DB 34, the good / bad determination regarding each learning construction site image, and the defect identified for the learning construction site determined to be defective. The learning device 36 is learned using learning data including types. That is, the learning processing unit 40, the output data for the learning construction site image, if for the learned construction site image is good decision, each unit of each layer as output variables y ₁ is the largest value 50 If the construction site image for learning is defective, the output variable corresponding to the type of failure of the learning construction site image is adjusted so that the value of the output variable is maximized. Each weight and bias of the unit 50 is adjusted.

  The determination unit 42 inputs a determination target construction site image as input data to the learning device 36 learned in this way. And the determination part 42 performs the quality determination of the said determination object construction site image based on the output data of the learning device 36, and also determines the kind of defect about the determination object construction site image used as the defect determination.

More specifically, the determination unit 42, the output data of the learning device 36, if the output variables y ₁ corresponding to the good judgment indicates the largest value, and good judging the determination target worksite image. The determination unit 42, if they exhibit the greatest value output variable y ₂ corresponding to the first type a defective defective judgment, the judgment target construction site image and defect determination, the kind of the defect The first type is determined. The determination unit 42, if they exhibit the greatest value output variable y ₃ corresponding to the second type a defective defective judgment, the judgment object construction site image and defective judgment, the kind of the defect It is determined that it is the second type. When the determination unit 42 repeats this process, a determination result including a good / bad determination and a defect type regarding a plurality of determination target construction site images is output for each site worker.

  The determination result of the learning device 36 is transmitted from the server 16 to the administrator terminal 14, and the display unit of the administrator terminal 14 not only determines whether the plurality of determination target construction site images are good or bad for each site worker, but also indicates a defect. The type of defect for the image is displayed. Thereby, the manager can grasp the tendency of defects such as a large number or a small number of specific types of defects for each field worker.

  Further, a plurality of learning devices 36 may be provided corresponding to each process of construction. For example, a learning device for process 1a, a learning device for process 2b, a learning device for process 3c, and so on may be provided. In this case, the learning processing unit 40 learns each learning device 36 using learning data including a learning construction site image in a process corresponding to each learning device 36. For example, for the learning device 36a for the process 1, learning is performed using learning data including the learning construction site image associated with the process 1 among the plurality of learning construction site images stored in the construction site image DB 34. I do.

  Based on the process identification information associated with the determination target construction site image, the determination unit 42 selects the learning device 36 that inputs the determination target construction site image from the plurality of learning devices 36. For example, the determination unit 42 selects the process 1 learning device 36a for the determination target construction site image associated with the process identification information indicating the process 1. Then, the determination unit 42 inputs the determination target construction site image to the selected learning device 36.

  As described above, each learning device 36 corresponding to each process outputs the determination result for each field worker based on the worker identification information associated with the determination target construction site image. As a result, the determination result related to the determination target construction site image is output for each site worker and for each process. By repeating this process, the determination unit 42 outputs a good / bad determination regarding a plurality of determination target construction site images for each set of site workers and processes. FIG. 6 shows a state in which the determination result related to the determination target construction site image is output separately for each site worker and for each process. In addition, each learning device 36 corresponding to each process may determine not only the quality of the determination target construction site image but also the type of failure for the defective image.

  The determination result of the learning device 36 is transmitted from the server 16 to the manager terminal 14, and the display unit of the manager terminal 14 determines whether or not the plurality of judgment target construction site images are acceptable for each set of site workers and processes. (And the type of defect) are displayed. That is, the display unit of the manager terminal 14 displays statistical data as to whether or not the construction result is appropriate while being distinguished for each group of site workers and processes. Thereby, the manager can grasp the tendency that there are many or few defects in a specific process for each field worker. For example, on-site worker A is good at process 1 while he is not good at process 2, and on-site worker B is good at process 2 while he is not good at process 1 Can do.

  As mentioned above, although embodiment which concerns on this invention was described, this invention is not limited to the said embodiment, A various change is possible unless it deviates from the meaning of this invention. For example, in the present embodiment, the site worker performs a good / bad determination regarding the new construction site image as a still image acquired by performing the shooting action, but after operating the shooting function (camera function) By holding the digital camera 12 over the subject (construction result), the new construction site image group is continuously transmitted to the server 16, and if there is a good judgment from the new construction site image group, the new construction site You may make it determine with the construction result corresponding to a field image group being good determination.

10 construction site image determination system, 12 digital camera, 14 administrator terminal, 16 server, 18 communication line, 30 communication unit, 32 storage unit, 34 construction site image DB, 36 learning device, 36a learning device for step 1 and 36b step 2 learning device, 36c process 3 learning device, 38 control unit, 40 learning processing unit, 42 determination unit, 50 units.

Claims (4)

By using learning data including multiple learning construction site images showing construction results and learning data including good / bad judgments for the construction results shown by each learning construction site image, it is possible to make good / bad judgments regarding construction site images. A simple learner,
The determination target construction site image is input to the learning device, the quality determination regarding the determination target construction site image is performed based on the output data of the learning device, and the determination target construction associated with the determination target construction site image is performed. Based on the worker identification information indicating the site worker who was in charge of the construction corresponding to the site image, the quality judgment regarding the judgment site image to be judged is output separately for each site worker. A determination unit that outputs a determination result including a good / bad determination regarding a determination target construction site image;
A construction site image determination apparatus comprising: The learning device further learns using the learning data including the type of defect specified for the learning construction site image for which the failure is determined, so that the quality determination and the classification of the type of defect regarding the construction site image are performed. Is possible,
When the determination unit determines a defect with respect to the determination target construction site image, the determination unit further determines the type of defect related to the determination target construction site image based on the output data of the learning device, and a plurality of each site worker Outputs judgment results for the construction site image to be judged, including good / bad judgment and type of defect,
The construction site image determination apparatus according to claim 1, wherein: A plurality of learning devices are provided corresponding to a plurality of construction steps,
Each learning device learns using a plurality of the learning data including the learning construction site image of the process corresponding to the self,
The determination unit inputs the determination target construction site image to a learning device selected based on process identification information associated with the determination target construction site image and indicating a construction process indicated by the determination target construction site image. Thus, the determination result relating to the determination target construction site image is output separately for each site worker and for each process, and the determination result regarding a plurality of determination target construction site images for each set of the site worker and the process. Output,
The construction site image determination apparatus according to claim 1 or 2, characterized in that Computer
By using learning data including multiple learning construction site images showing construction results and learning data including good / bad judgments for the construction results shown by each learning construction site image, it is possible to make good / bad judgments regarding construction site images. A simple learner,
The determination target construction site image is input to the learning device, the quality determination regarding the determination target construction site image is performed based on the output data of the learning device, and the determination target construction associated with the determination target construction site image is performed. Based on the worker identification information indicating the site worker who was in charge of the construction corresponding to the site image, the quality judgment regarding the judgment site image to be judged is output separately for each site worker. A determination unit that outputs a determination result including a good / bad determination regarding a determination target construction site image;
Construction site image determination program characterized by functioning as

Images