Automated imaging system for object footprint detection and a method thereof
The present disclosure provides for a system for facilitating a completely automated process that may directly fetch an imagery of a given location and area from any mapping module and extract a plurality of objects in the given imagery. Further, a deep learning-based object segmentation such as but not limited to a cascaded reverse mask RCNN framework method may generate a set of predefined vectors associated with the image. The system may be configured to automate the generation of the predefined vectors based on the image received from the image sensing assembly.
1 . An automated imaging sensing system, said system comprising:
an image module comprising one or more processors, wherein the one or more processors are coupled with a memory, wherein said memory stores instructions which when executed by the one or more processors causes said system to:
receive a set of images of an object from an image sensing assembly, wherein the set of images are obtained at a plurality of viewpoints;
extract, by using a Deep Learning (DL) engine, a first set of attributes from each image in the set of images recorded at respective viewpoints based on a location template, wherein the first set of attributes pertain to center coordinates and radius of the region of interest of each image at the respective viewpoints, wherein the DL engine is operatively coupled to the one or more processors;
extract, by using the DL engine, a second set of attributes from the first set of attributes extracted, the second set of attributes pertaining to a set of predefined boundaries associated with the object;
generate, by using the DL engine, a mask for each set of predefined boundaries of the object, wherein the mask is generated to assign each pixel of the image to one of the predefined boundaries of the object, and to capture geometric properties of the object;
merge, by the DL engine, the mask of each set of predefined boundaries of the object with each other to obtain a set of predefined vectors to be stored in a database associated with the system;
cascade, by a segmentation module coupled with the DL engine, a multi-class segmentation task to generate at least four different pixels masks for background, object interior, object exterior and object separator pixel masks in a hierarchal manner;
merge, by a post processing module the generated at least four different pixels masks to generate a single building polygon mask for each building; and
convert, by a vectorization module the generated polygon masks into geospatial shape files for geospatial database ingestion.
2 . The system as claimed in claim 1 , wherein the plurality of viewpoints refers to coordinates and radius of an object or region of interest, latitude, longitude of a region.
3 . The system as claimed in claim 1 , wherein the set of predefined boundaries comprises background, object interior, object edges and object separators.
4 . The system as claimed in claim 1 , wherein an object detection module is operatively coupled to the one or more processors, wherein the object detection module is configured to:
process the extracted second set of attributes;
obtain a set of features from the processed second set of attributes;
map down one or more precise unique features of the object from the set of features obtained.
5 . The system as claimed in claim 1 , wherein the DL engine is further configured to:
obtain a set of values of each image of the set of images;
process the set of values of each said image to yield a set of predefined vectors; and
generate a trained model configured from the set of predefined vectors.
6 . The system as claimed in claim 5 , wherein the DL engine is further configured to:
automate fetching of an image from the image sensing assembly to generate the predefined set of vectors specific to the image.
7 . The system as claimed in claim 5 , wherein the set of values of each image are any or a combination of red green blue (RGB) values, greyscale values, luma, and blue projection and red projection (YUV) values.
8 . The system as claimed in claim 5 , wherein the trained model is trained to take an image automatically as an input and return a minimum rotated bounding box for the object along with one or more pixel labels associated with the object.
9 . The system as claimed in claim 1 , wherein the image sensing assembly comprises one or more analog electronic input source configured for recording a plurality of physical parameters simultaneously with the set of images and a network connecting one or more camera sensors and the one or more analog input sources to the computing device.
10 . A method for facilitating automated image sensing, said method comprising:
receiving, by an image module a set of images of an object from an image sensing assembly, wherein the set of images are obtained at a plurality of viewpoints, wherein the image module comprises one or more processors, wherein the one or more processors are coupled with a memory, wherein said memory stores instructions which are executed by the one or more processors;
extracting, by using a Deep Learning (DL) engine, a first set of attributes from each image in the set of images recorded at respective viewpoints based on a location template, wherein the first set of attributes pertain to center coordinates and radius of the region of interest of each image at the respective viewpoints, wherein the DL engine is operatively coupled to the one or more processors;
extracting, by using the DL engine, a second set of attributes from the first set of attributes extracted, the second set of attributes pertaining to a set of predefined boundaries associated with the object;
generating, by using the DL engine, a mask for each set of predefined boundaries of the object, wherein the mask is generated to assign each pixel of the image to one of the predefined boundaries of the object, and to capture geometric properties of the object;
merging, by the DL engine, the mask of each set of predefined boundaries of the object with each other to obtain a set of predefined vectors to be stored in a database associated with the system;
cascading, by a segmentation module, a multi-class segmentation task to generate at least four different pixels masks for background, object interior, object exterior and object separator pixel masks in a hierarchal manner;
merging, by a post processing module the generated at least four different pixels masks to generate a single building polygon mask for each building; and
converting, by a vectorization module the generated polygon masks into geospatial shape files for geospatial database ingestion.
11 . The method as claimed in claim 10 , wherein the plurality of viewpoints refers to coordinates and radius of an object or region of interest, latitude, longitude of a region.
12 . The method as claimed in claim 10 , wherein the set of predefined boundaries comprises background, object interior, object edges and object separators.
13 . The method as claimed in claim 10 , wherein an object detection module is operatively coupled to the one or more processors, wherein the method further comprises the steps of:
processing, by the object detection module, the extracted second set of attributes;
obtaining, by the object detection module, a set of features from the processed second set of attributes; and,
mapping down, by the object detection module, one or more precise unique features of the object from the set of features obtained.
14 . The method as claimed in claim 10 , wherein the method further comprises the steps of:
obtaining, by the DL engine, a set of values of each image of the set of images;
processing, by the DL engine, the set of values of each said image to yield a set of predefined vectors; and
generating, by the DL engine, a trained model configured from the set of predefined vectors.
15 . The method as claimed in claim 14 , wherein the method further comprises the steps of:
automate fetching of an image, by the DL engine, from the image sensing assembly to generate the predefined set of vectors specific to the image.
16 . The method as claimed in claim 14 , wherein the set of values of each image are any or a combination of red green blue (RGB) values, greyscale values, luma, and blue projection and red projection (YUV) values.
17 . The method as claimed in claim 14 , wherein the trained model is configured to take an image automatically as an input and return a minimum rotated bounding box for the object along with one or more pixel labels associated with the object.
18 . The method as claimed in claim 10 , wherein the imaging sensing assembly comprises one or more analog electronic input source configured for recording a plurality of physical parameters simultaneously with the set of images and a network connecting one or more camera sensors and the one or more analog input sources to the computing device.