MACHINE LEARNING REAL PROPERTY OBJECT DETECTION AND ANALYSIS APPARATUS, SYSTEM, AND METHOD
Physical and logical components of an apparatuses, systems and methods for and related to detecting, identifying, and categorizing construction site objects and other objects on real property (“objects”) through artificial intelligence machine learning analysis of object sensor data to identify an object in the object sensor data, to determine a categorization of the object, to determine at least one of a site map, a hazardous condition, a theft, and or a behavior of the objects and to output warnings and utilization reports with respect to equipment, vehicles, personnel.
1 . A system to determine a behavior of an object through visual analysis of a construction site comprising:
a computer processor and a memory;
a runtime object time-series analysis module in the memory, and wherein to determine the behavior of the object, the computer processor is to execute the runtime object time-series analysis module, is to process an object tensor with a time-series neural network of the runtime object time-series analysis module and is to output the behavior of the object from the runtime object time-series analysis module, wherein the object tensor encodes at least one of an image portion corresponding to the object, the object, and a categorization of the object.
2 . The system according to claim 1 , wherein the runtime object time-series analysis module comprises a time-series neural network.
3 . The system according to claim 2 , wherein the time-series neural network comprises a long short-term memory recurrent neural network.
4 . The system according to claim 1 , further comprising a runtime object identification module in the memory, wherein the runtime object identification module is to identify the object and is to determine the categorization of the object through visual analysis of the construction site, wherein to identify the object and to determine the categorization of the object through visual analysis of the construction site, the computer processor is to execute the runtime object identification module, obtain an object sensor data with respect to the construction site, identify the object in the object sensor data, and determine the categorization of the object, wherein the object sensor data comprises an image of the construction site.
5 . The system according to claim 4 , wherein the computer processor is further to execute the runtime object identification module and output the object tensor.
6 . The system according to claim 5 , wherein the object tensor encodes at least one of an image portion corresponding to the object, the object, and the categorization of the object.
7 . The system according to claim 4 , wherein the runtime object identification module further comprises an object detection neural network.
8 . The system according to claim 7 , wherein the object detection neural network comprises a convolutional neural network.
9 . A method to distinguish a behavior of an object through visual analysis of a construction site, comprising:
obtaining a plurality of images of the construction site;
visually analyzing at least one of the plurality of images of the construction site with an object detection neural network and, based thereon, identifying the object, determining a categorization of the object, and outputting a plurality of object tensors;
processing the plurality of object tensors with a time-series neural network and outputting the behavior of the object; and
distinguishing at least one of the object, the object category, or the behavior in a visual output.
10 . The method according to claim 9 , further comprising stitching together at least a subset of the plurality of images to form a composite image of the construction site.
11 . The method according to claim 10 , wherein the visual output comprises the composite image of the construction site and further comprising distinguishing at least one of the object, the object category, or the behavior in the visual output comprising the composite image of the construction site.
12 . The method according to claim 9 , wherein the object tensor encodes at least one of an image portion corresponding to the object, the object, and a categorization of the object.
13 . The method according to claim 9 , wherein the time-series neural network comprises a long short-term memory recurrent neural network.
14 . The method according to claim 9 , wherein the object detection neural network comprises a convolutional neural network.
15 . An apparatus to distinguish a behavior of an object through visual analysis of a construction site, comprising:
means to obtain a plurality of images of the construction site;
means to visually analyze at least one of the plurality of images of the construction site with an object detection neural network and, based thereon, means to identify the object, determine a categorization of the object, and output a plurality of object tensors;
means to process the plurality of object tensors with a time-series neural network and means to output the behavior of the object; and
means to distinguish at least one of the object, the object category, or the behavior in a visual output.
16 . The apparatus according to claim 15 , further comprising means to stitch together at least a subset of the plurality of images to form a composite image of the construction site.
17 . The apparatus according to claim 16 , wherein the visual output comprises the composite image of the construction site and further comprising means to distinguish at least one of the object, the object category, or the behavior in the visual output comprising the composite image of the construction site.
18 . The apparatus according to claim 15 , wherein the object tensor encodes at least one of an image portion corresponding to the object, the object, and a categorization of the object.
19 . The apparatus according to claim 15 , wherein the time-series neural network comprises a long short-term memory recurrent neural network.
20 . The apparatus according to claim 15 , wherein the object detection neural network comprises a convolutional neural network.