Object recognition system with invisible or nearly invisible lighting
A barcode reader is provided. The barcode reader includes a first image acquisition assembly having a first imager assembly configured to capture infrared (IR) light and an IR illumination assembly configured to emit IR light over at least a portion of a first field of view (FOV) of the first imager assembly so as to illuminate targets within the first FOV. The barcode reader further includes a second image acquisition assembly having a second imager assembly configured to capture visible light and a visible-light illumination assembly configured to emit visible light over at least a portion of a second FOV of the second imager assembly so as to illuminate targets within the second FOV.
1. An object recognition system comprising:
a bi-optic barcode reader comprising:
a housing having a platter and an upright tower, the platter having a generally horizontal window and the upright tower having a generally upright window;
a first image acquisition assembly positioned at least partially within the housing, the first image acquisition assembly having an infrared (IR) illumination assembly and a first imager assembly, the first imager assembly having a first field of view (FOV) and being configured to capture IR light, the first FOV being directed out of the housing through the generally upright window, the IR illumination assembly being configured to emit IR light over at least a portion of the first FOV so as to illuminate targets within the first FOV; and
a second image acquisition assembly positioned at least partially within the housing, the second image acquisition assembly having a visible-light illumination assembly and a second imager assembly, the second imager assembly having a second FOV and being configured to capture visible light, the second FOV being directed out of the housing through the generally horizontal window, the visible-light illumination assembly being configured to emit visible light over at least a portion of the second FOV so as to illuminate targets within the second FOV; and
a server communicatively coupled to the bi-optic barcode reader, the server configured to:
train a machine-learning object recognition model by analyzing first image data received from the first image acquisition assembly; and
generate, via the machine-learning object recognition model, object recognition data in response to receiving second image data from the first image acquisition assembly.
2. The object recognition system of claim 1 , wherein the first image data and the second image data at least partially overlap.
3. The object recognition system of claim 1 , wherein the bi-optic barcode reader further comprises:
a controller communicatively coupled to the first image acquisition assembly and the second image acquisition assembly, the controller having instructions programmed therein which, when executed, cause the controller to instruct the IR illumination assembly and the visible-light illumination assembly to alternatively activate.
4. The object-recognition system of claim 1 , wherein the first FOV and the second FOV at least partially intersect in a target-scanning region above the platter and in front of the upright tower.
5. The object-recognition system of claim 1 , wherein the first imager assembly includes a monochrome imager, and wherein the second imager assembly includes a multi-colored imager.
6. A method, comprising:
capturing, by a first imager assembly of a first image acquisition assembly positioned at least partially within a housing having a platter and an upright tower, the platter having a generally horizontal window and the upright tower having a generally upright window, images of targets within a first FOV illuminated by infrared (IR) light emitted by an IR illumination assembly of the first image acquisition assembly, wherein the first FOV is directed out of the housing through the generally upright window;
capturing, by a second imager assembly of a second image acquisition assembly positioned at least partially within the housing, images of targets within a second FOV illuminated by visible light emitted by a visible-light illumination assembly of the second image acquisition assembly, wherein the second FOV is directed out of the housing through the generally horizontal window;
training, by a server communicatively coupled to the bi-optic barcode reader, a machine-learning object recognition model by analyzing first image data received from the first image acquisition assembly; and
generating, by the server, via the machine-learning object recognition model, object recognition data in response to receiving second image data from the first image acquisition assembly.
7. The method of claim 6 , further comprising:
instructing, by a controller communicatively coupled to the first image acquisition assembly and the second image acquisition assembly, the IR illumination assembly and the visible-light illumination assembly to alternatively activate.
8. The method of claim 6 , wherein the first FOV and the second FOV at least partially intersect in a target-scanning region above the platter and in front of the upright tower.
9. The method of claim 6 , wherein the first imager assembly includes a monochrome imager, and wherein the second imager assembly includes a multi-colored imager.
10. A method, comprising:
capturing, by a first imager assembly of a first image acquisition assembly positioned at least partially within a housing having a window, images of targets within a first FOV illuminated by infrared (IR) light emitted by an IR illumination assembly of the first image acquisition assembly, wherein the first FOV is directed out of the housing through the window;
capturing, by a second imager assembly of a second image acquisition assembly positioned at least partially within the housing, images of targets within a second FOV illuminated by visible light emitted by a visible-light illumination assembly of the second image acquisition assembly, wherein the second FOV is directed out of the housing through the window;
training, by a server communicatively coupled to the barcode reader, a machine-learning object recognition model by analyzing first image data received from the first image acquisition assembly; and
generating, by the server, via the machine-learning object recognition model, object recognition data in response to receiving second image data from the first image acquisition assembly.
11. The method of claim 10 , further comprising:
instructing, by a controller communicatively coupled to the first image acquisition assembly and the second image acquisition assembly, the IR illumination assembly and the visible-light illumination assembly to alternatively activate.
12. The method of claim 10 , wherein the first FOV and the second FOV at least partially intersect in a target-scanning region.
13. The method of claim 10 , wherein the first imager assembly includes a monochrome imager, and wherein the second imager assembly includes a multi-colored imager.
14. An object recognition system comprising:
a barcode reader comprising:
a housing having a window;
a first image acquisition assembly positioned at least partially within the housing, the first image acquisition assembly having an infrared (IR) illumination assembly and a first imager assembly, the first imager assembly having a first field of view (FOV) and being configured to capture IR light, the first FOV being directed out of the housing through the window, the IR illumination assembly being configured to emit IR light over at least a portion of the first FOV so as to illuminate targets within the first FOV; and
a second image acquisition assembly positioned at least partially within the housing, the second image acquisition assembly having a visible-light illumination assembly and a second imager assembly, the second imager assembly having a second FOV and being configured to capture visible light, the second FOV being directed out of the housing through the window, the visible-light illumination assembly being configured to emit visible light over at least a portion of the second FOV so as to illuminate targets within the second FOV; and a server communicatively coupled to the barcode reader, the server configured to:
train a machine-learning object recognition model by analyzing first image data received from the first image acquisition assembly; and
generate, via the machine-learning object recognition model, object recognition data in response to receiving second image data from the first image acquisition assembly.
15. The object recognition system of claim 14 , wherein the first image data and the second image data at least partially overlap.
16. The object recognition system of claim 14 , wherein the barcode reader further comprises:
a controller communicatively coupled to the first image acquisition assembly and the second image acquisition assembly, the controller having instructions programmed therein which, when executed, cause the controller to instruct the IR illumination assembly and the visible-light illumination assembly to alternatively activate.
17. The object-recognition system of claim 14 , wherein the first FOV and the second FOV at least partially intersect in a target-scanning region.
18. The object-recognition system of claim 14 , wherein the first imager assembly includes a monochrome imager, and wherein the second imager assembly includes a multi-colored imager.