DETERMINATION DEVICE, DETERMINATION METHOD, AND PROGRAM
A determination device includes an image information acquirer configured to acquire image information of a subject image obtained by photographing an internal space of a toilet bowl in excretion; an estimator configured to perform estimation regarding a determination matter relating to excretion by inputting the image information to a learned model, the learned model having learned a correspondence relationship between an image for learning and a determination result of the determination matter relating to excretion, the learned model learned by machine learning using a neural network, the image for learning representing an internal space of a toilet bowl in excretion; and a determiner configured to perform determination regarding the determination matter of the subject image based on an estimation result obtained by the estimator.
1 . A determination device, comprising:
an image information acquirer configured to acquire image information of a subject image obtained by photographing an internal space of a toilet bowl in excretion;
an estimator configured to perform estimation regarding a determination matter relating to excretion by inputting the image information to a learned model, the learned model having learned a correspondence relationship between an image for learning and a determination result of the determination matter relating to excretion by machine learning using a neural network, the image for learning representing an internal space of a toilet bowl in excretion; and
a determiner configured to perform determination regarding the determination matter of the subject image based on an estimation result obtained by the estimator.
2 . The determination device of claim 1 , wherein the subject image is an image obtained by photographing the internal space of the toilet bowl after excretion.
3 . The determination device of claim 1 , wherein the determination matter includes at least one of presence-absence of urine, presence-absence of stools, and properties of stools.
4 . The determination device of claim 1 , wherein the determination matter includes use-unuse of paper in excretion and an amount of usage of paper in a case where paper has been used.
5 . The determination device of claim 1 , wherein the determiner determines a flushing method for flushing a toilet under a situation indicated by the subject image.
6 . The determination device of claim 5 ,
wherein the determination matter includes at least one of properties of stools and an amount of usage of paper in excretion,
the estimator estimates at least any one of properties of stools in the subject image and the amount of usage of paper in excretion in the subject image, and
the determiner determines the flushing method for flushing the toilet under the situation indicated by the subject image based on at least any one of the properties of stools and the amount of usage of paper in excretion, the properties of stools and the amount of usage of paper in excretion are estimated by the estimator.
7 . The determination device of claim 1 , wherein the determination matter includes determination of whether or not excretion has been performed.
8 . The determination device of claim 1 , wherein
the determination device is configured to be connected with a toilet device including the toilet bowl, a toilet seat and a human's bottom washing device the determiner performs determination regarding the determination matter at predetermined time intervals until a predetermined end condition is satisfied after a predetermined start condition is satisfied,
the start condition is to detect that a user has sat on the toilet seat of the toilet device, and
the end condition is at least any one of use of the human's bottom washing device of the toilet device, an operation of flushing the toilet bowl of the toilet device, and detection of the user standing up from the toilet seat of the toilet device.
9 . The determination device of claim 1 , wherein the determination matter includes determination of whether or not dirt is photographed in the subject image, the dirt is due to an image pickup device or an image pickup environment.
10 . The determination device of claim 9 , wherein
the determination matter includes at least any one of presence-absence of urine, presence-absence of stools, and properties of stools, and
the determiner does not perform determination of any one of presence-absence of urine, presence-absence of stools, and properties of stools when the estimator has estimated that the dirt is photographed.
11 . The determination device of claim 9 , wherein
the determination matter includes at least any one of presence-absence of urine, presence-absence of stools, and properties of stools, and
the determiner performs any one of determination of presence-absence of urine, presence-absence of stools, and properties of stools by using a learned model when the estimator has estimated that the dirt is photographed, the learned model has learned a correspondence relationship between the image for learning and a determination result of the determination matter relating to excretion by machine learning using a neural network, the image for learning includes the dirt.
12 . The determination device of claim 9 , wherein
the determiner outputs information indicating dirt to a destination set in advance when the estimator has estimated that the dirt is photographed.
13 . A determination method for determining a determination matter relating to excretion, the determination method comprising:
acquiring image information of a subject image obtained by photographing an internal space of a toilet bowl in excretion by an image information acquirer;
performing estimation regarding the determination matter of the subject image by an estimator by inputting the image information to a learned model, the learned model having learned a correspondence relationship between an image for learning and a determination result of the determination matter relating to excretion by machine learning using a neural network, the image for learning representing an internal space of a toilet bowl in excretion; and
performing, by a determiner, determination regarding the determination matter of the subject image based on an estimation result obtained by the estimator.
14 . A non-transitory computer readable storage medium that stores a program for causing computer executable instructions, when executed by one or more computers, the one or more computers comprising:
acquiring image information of a subject image obtained by photographing an internal space of a toilet bowl in excretion;
performing estimation regarding the determination matter of the subject image by an estimator by inputting the image information to a learned model, the learned model having learned a correspondence relationship between an image for learning and a determination result of the determination matter relating to excretion by machine learning using a neural network, the image for learning representing an internal space of a toilet bowl in excretion; and
performing determination regarding the determination matter of the subject image based on a result of the estimation.