Single fiber optic color identification

Abstract

To provide single optical fiber color marking, and a system and techniques for determining a color of an optical fiber in a fiber optic cable.SOLUTION: A spectrophotometer camera may obtain a color value of the optical fiber. A fiber adaptor operably holds a single optical fiber of a fiber optic cable in a field of view of the spectrophotometer camera. In a memory, instructions are stored that, when executed by a processor, make it possible to identify a color of the optical fiber. The color value may be compared to a color value of a number of reference colors. A color match score value may be generated for the color value with respect to each reference color. A confidence value may be obtained for a pair of color match scores that are closest in the score value. Based on the confidence value, one of the reference colors may be identified as a color of the optical fiber.SELECTED DRAWING: Figure 4

Description

[Background technology]

[0001] Global communications (both voice and data) are primarily transmitted via undersea fiber optic cables. Growing demands for higher network capacity are driving system providers to design undersea cables and other technologies with an ever-increasing number of optical fibers. One of the factors limiting the number of optical fibers in a cable is the ability to identify a specific fiber from a large number of other fibers. Optical fibers in a cable must be uniquely identifiable to enable operation within the cable system. This unique identification is achieved by coloring each fiber with a unique color. In recent years, transoceanic cable systems with fiber counts of 18 or more paths have become common. Cable operators are trained to visually identify each fiber by color. However, this human visual identification process can be prone to errors (and such errors can be very expensive to correct), especially when the number of unique colors increases beyond 12.

[0002] One technique that contributes to color separation is to apply a ring-like identifier to overlapping colors. However, this can cause increased microbending losses along the optical fiber. Small incremental fiber attenuation can cause wiring losses over the length of the system. Adding repeaters increases costs and likely reduces the overall system efficiency.

[0003] Therefore, a solution that can uniquely identify the color on a single strand of optical fiber (especially in high fiber count cables) in a reliable, non-destructive, and user-friendly manner, without the potential performance degradation associated with ring beacons or similar solutions, would address one of the major challenges of expanding the fiber count in submarine cables. Implementing a solution that quantifies the fiber identification process would improve quality and ultimately provide cost benefits by addressing the factor of human error.

[0004] Color spectrophotometers are widely used in various color measurement applications. These devices measure the intensity of light reflected from a sample surface at each wavelength in the visible spectrum. The data value of the illuminant (light source) and the target reflectance are treated as a set of triplet values ​​that represent color. The accuracy of such measurements is affected by the amount of reflected light captured by the lens aperture of the device. Therefore, the surface area of ​​the sample plays a key role in obtaining an accurate color reading.

[0005] However, the cable industry has traditionally relied on the subjective analysis (human eye) of fiber optic operators to distinguish fiber optic color. With cables containing large numbers of fibers with different colors, it has become increasingly difficult to separate more than 12 colors using conventional technology.

[0006] Traditionally, the number of optical fibers in a submarine cable is small (12 or fewer). Training cable operators to visually distinguish between 12 unique colors is a relatively easy task. When necessary, color plates are used as a reference to minimize errors at splicing or repair stations. When the number of optical fibers is relatively large (usually in relatively short branch segments), some fibers are marked with rings. Therefore, the need to identify each optical fiber color is largely met without considering a mechanism for measuring color on a single optical fiber.

[0007] A common technique for measuring colors on optical fibers is to stack these fibers on top of each other to form an array with a surface area large enough to cover the aperture of a color spectrophotometer. (In some specialized applications, the minimum aperture size is approximately 8 mm in diameter.) While this method is sometimes used by fiber optic suppliers, it is not a practical solution for most field operations (such as cable installations or subsea deployments). Stacking multiple optical fibers into an array requires a minimum of one meter of excess optical fiber, which is then cut into shorter lengths to construct the fiber optic array and then measured using a color spectrophotometer. Typically, cable operators are unlikely to have this much excess optical fiber available. Additionally, constructing a fiber optic array adds significant time to each measurement, making it impractical for time-sensitive field or factory operations.

[0008] To date, quantifiable methods for identifying fiber optic color have not been applied to cables with relatively few optical fibers due to the lack of necessary tools (although the naked eye is usually sufficient to distinguish a small number of colors) and the limitations of spectrophotometers, which are typically unable to measure color on surfaces smaller than 2 mm.

[0009] Previous fiber optic color measurement techniques are suited to laboratory environments. Under these conditions, sufficient fiber lengths can be used to provide a large enough surface area to measure fiber optic arrays with a spectrophotometer using conventional techniques. Furthermore, with a typical colored fiber having an outer diameter of 250 μm, a single fiber presents a very small surface area. Therefore, standard measurement techniques using a color spectrophotometer are insufficient to provide a practical method for fiber optic identification via color matching.

[0010] It would be beneficial to have an automated technique for accurately identifying each individual optical fiber in a high fiber count cable. The following description provides examples of systems, devices, and processes that can be used to accurately identify each optical fiber. Summary of the Invention

[0011] In one aspect, a method for identifying the color of an optical fiber is disclosed. The method may include, by a processor, obtaining color values ​​of an optical fiber in a fiber optic cable from a spectrophotometer camera. The processor may compare the color values ​​of the optical fiber with color values ​​of each reference color of a plurality of reference colors, where each reference color has a unique color value. Based on the results of the comparison, the processor may generate a color match score for the color value of the optical fiber for each reference color. The color values ​​of each reference color are different for each reference color, and the color match score has a score value. The processor may obtain a confidence value for the pair of color match scores with the closest score value. Based on the confidence value, one of the plurality of reference colors may be identified as the color of the optical fiber.

[0012] In another aspect, a system is disclosed that includes a spectrophotometer camera, a fiber optic adapter, a processor, and a memory. The system further includes a fiber adapter that operably holds a single optical fiber of a fiber optic cable in a field of view of the spectrophotometer camera. The memory includes instructions stored therein that, when executed by the processor, cause the system to acquire color values ​​of the optical fibers in the fiber optic cable from the spectrophotometer camera. The processor compares the color value of the optical fiber with color values ​​of each reference color of a plurality of reference colors, where each reference color has a unique color value. Based on the results of the comparison, a color match score may be generated for the color value of the optical fiber for each reference color of the plurality of reference colors, where the color value of each reference color is different for each reference color and the color match score has a score value. A confidence value may be obtained for the pair of color match scores that have the closest score value. The processor may identify one of the plurality of reference colors as the color of the optical fiber of the fiber optic cable based on the confidence value.

[0013] In yet another aspect, a non-transitory computer-readable storage medium is provided. The computer-readable storage medium includes instructions that, when executed by a processor, cause the processor to read a sample color from optical fibers in a fiber optic cable. The sample color has a color value. The processor may select a color matching algorithm from a plurality of color matching algorithms and input the color value of the color sample to the selected color matching algorithm. The selected color matching algorithm processes the input color value against a plurality of reference colors. The processor may generate a color match score for each reference color from the plurality of reference colors. The processor may generate a confidence value based on a ratio of the two closest color match scores. If another color matching algorithm from the plurality of color matching algorithms is available for selection, the process of executing, selecting, inputting, generating another color match score, and generating another confidence value for the matching algorithm is repeated. If no other color matching algorithm is available, the processor may identify a maximum confidence value from the generated confidence values, select a color corresponding to the identified maximum confidence value as the color of the optical fibers in the fiber optic cable, and generate an output indicating the selected color. [Brief explanation of the drawings]

[0014] To easily identify a particular element or discussion of an operation, the most significant digit(s) of a reference number refers to the figure number in which that element is first introduced.

[0015] [Figure 1A] 1 shows an example of a camera system used for color identification of a single optical fiber.

[0016] [Figure 1B] 1B illustrates an example aperture of the camera of FIG. 1A according to an embodiment.

[0017] [Figure 1C] 1B illustrates another example of an aperture of the camera of FIG. 1A according to another embodiment.

[0018] [Figure 2A] 2 shows a top view of an example fiber optic adapter that can be used for color identification of a single optical fiber in conjunction with the camera system of FIG. 1.

[0019] [Figure 2B] 2B shows a bottom view of the example fiber optic adapter of FIG. 2A.

[0020] [Figure 2C] 10 shows a top view of another example of a fiber optic adapter that can be used for color identification of a single optical fiber in conjunction with the camera system of FIG. 1.

[0021] [Figure 2D] 2D shows a plan view of another example of the optical fiber adapter of FIG. 2C that can be used for color identification of a single optical fiber in conjunction with the camera system of FIG. 1.

[0022] [Figure 2E] 2D shows a bottom view of another example of the fiber optic adapter of FIG. 2C that can be used for color identification of a single optical fiber in conjunction with the camera system of FIG. 1.

[0023] [Figure 3] 1 illustrates an example of a camera system having a fiber optic adapter configured for use in color identification of a single optical fiber.

[0024] [Figure 4] 1 illustrates a flowchart of an example process of the disclosed subject matter in the examples.

[0025] [Figure 5] 1 shows an example of a color matching algorithm according to an embodiment.

[0026] [Figure 6] 10 illustrates another example of a color matching algorithm according to an embodiment.

[0027] [Figure 7] An example of a reference color entry database (L*a*b*) is shown.

[0028] [Figure 8] 10 illustrates a flowchart of another example process according to an embodiment.

[0029] [Figure 9] 1 shows a functional block diagram of a color matching system that can be used to implement the examples described with reference to FIGS. 1 to 8. DETAILED DESCRIPTION OF THE INVENTION

[0030] The disclosed systems, devices, and processes provide a novel technique for applying traditional colorimetric techniques to distinguish colors on very small surfaces of optical fibers. The solution is cost-effective, does not require precise setup, provides instant results, is reliable, non-destructive, and is optimized for field applications requiring optical fiber lengths of less than about 2 inches.

[0031] The quantification method presented herein eliminates excessive reliance on human operators to identify fiber optic color and can significantly reduce errors, thereby minimizing cross-splices, resulting in significant time and cost savings and improving overall production quality.

[0032] The embodiments described herein have the advantage of requiring little to no fiber optic sample preparation, unlike conventional array-based fiber optic color measurements (which require at least about 1 meter of sacrificial fiber optic lead and a large amount of operator time to construct the fiber optic array).

[0033] The technology outlined here describes how a color spectrophotometer can be used to uniquely identify colors on a single optical fiber.

[0034] The solution described herein is a mechanism for accurately identifying the color of a single optical fiber in a manner suitable for use in a factory or cable delivery environment (fast, portable, and easy to use). Figure 1A shows an example of a camera system used for single-fiber color identification. The measurement mechanism includes a fiber-optic holding device built into a color spectrophotometer camera (e.g., a modified CM-700d by Konica Minolta Sensing camera 100) in combination with a color-matching algorithm that identifies the fiber color by matching measurements to a database of predetermined values ​​for each color. The camera system 100 may include a camera 102, an aperture 104, and a screw 106. The aperture 104 is a customized aperture configured to limit the camera's field of view to a size suitable for sampling the optical fibers of a fiber optic cable. The screw 106 attaches a collar 105 around the aperture 104 to the camera 102. Collar 105 may be a circular concentric dish or the like.

[0035] As an example, a spectrophotometer (e.g., camera 102) quantifies color by representing it as three values—L* (luminance), a* (red-green), and b* (blue-yellow)—in a three-dimensional CIE rectangular color space. Of course, other color spaces, such as RGB, HSV, HSL, and YPbPr, may also be used. Additionally, spectrophotometer camera 102 records a spectrophotometric curve, which is the amount of reflected light at each wavelength between 400 nm and 700 nm (the visible light range) measured for each sample. Both the L*, a*, and b* values ​​and the spectrophotometric curve data values ​​for a given optical fiber sample measurement can be used to identify optical fiber color by combining the acquired color value data with a lookup table containing predetermined reference values ​​for each optical fiber color used in constructing optical fiber cables. By way of example, optical fiber colors used in fiber optic cables may include red, blue, yellow, brown, green, orange, violet, black, rose, aqua, olive, white, lime, tan, magenta, gray, natural, dark green, lavender, purple, sky blue, pink, peach, and saffron.

[0036] FIG. 1B shows an example of a collar over an apertured cap for use with the spectrophotometer camera of FIG. 1A. The apertured cap 108 may be fastened over a color spectrophotometer camera light source (such as a modified CM-700d Konica Minolta Sensing camera 100 of FIG. 1A). The collar may be operably attached to the cap 108 via a retaining element (in this example, screw 116). The aperture 110 may have a diameter of approximately 2 mm. The 2 mm diameter of the aperture 110 is selected to limit the field of view of the camera 110 to sampling a single optical fiber.

[0037] FIG. 1C shows another example of an aperture for use with the spectrophotometer camera of FIG. 1A. Similar to cap 108, cap 112 may be fastened over a color spectrophotometer light source (such as a modified CM-700d version of Konica Minolta Sensing camera 100 of FIG. 1A). The collar may be operably attached to cap 108 via a retaining element (in this example, screw 116). Aperture 112 may have a size of approximately 1 mm by approximately 3 mm. The size of aperture 112 may be selected to limit the field of view of camera 110 for sampling a single optical fiber.

[0038] FIG. 2A shows a top view of an example fiber optic adapter that can be used with the camera system of FIG. 1A for color identification of a single optical fiber. The adapter system 200 includes a fiber optic adapter 202 and a retaining element 206. An optical fiber 204 is positioned to pass through an opening 208 on each side of the fiber optic adapter 202. In some examples, there may be multiple retaining elements 206. In the example shown, there may be three retaining elements 206, which may be magnetic material, dimples, low-adhesion adhesive dots, etc., in or on the fiber optic adapter 202. The retaining elements 206 are operable to maintain a constant pressure over an aperture (such as aperture 104 in FIG. 1A).

[0039] The fiber optic adapter 202 is shown as circular, and the aperture 104 in FIG. 1A is also circular, but is a customized aperture configured to limit the field of view of the camera 102 to sampling relative to the diameter of the optical fiber 204.

[0040] Figure 2B shows a bottom view of the example fiber optic adapter of Figure 2A. Fiber optic adapter 202 includes a cap 210 that mounts fiber optic adapter 202 over aperture 104 and is held in place by retaining element 206 and fiber optic groove 212. Fiber optic groove 212 is shown with fiber optic 204 positioned within fiber optic groove 212.

[0041] In an operational example, the optical fiber under test (204) threads through an opening 208 in the fiber optic adapter (202). The fiber optic groove 212 in the adapter is located directly above the circular aperture (4). This allows the fiber optic sample (1) to be repositioned above the circular aperture (4). The opaque inner surface of the cap 210 of the fiber optic adapter 202 is manipulated to ensure that the area outside the fiber optic surface is effectively shielded from affecting the sampling of the fiber optic 204. The fiber optic adapter 202 is positioned over a 2 mm aperture in the spectrophotometer, such as 302 shown in FIG. 3.

[0042] FIG. 2C shows a top view of another example of a fiber optic adapter that can be used for color identification of a single optical fiber in conjunction with the camera system of FIG. 1A.

[0043] The fiber optic adapter 214 includes a fiber optic groove 216 and a guide 218. The guide 218 allows an operator to insert a single fiber optic (not shown in this example) into the fiber optic groove 216. The fiber optic adapter 214 with the single fiber optic in the fiber optic groove 216 may be attached to a spectrophotometer camera (e.g., the camera shown in the disclosed example). The fiber optic adapter 214 is operable to hold the single fiber optic in the field of view of the spectrophotometer camera with a modified aperture, as shown in other examples.

[0044] Figure 2D shows a top view of another example of the fiber optic adapter of Figure 2C that can be used for color identification of a single optical fiber in conjunction with the camera system of Figure 1 A. Fiber optic adapter 214 is shown, with fiber optic groove 216 visible, and guide 218 shown as a recess in the side of fiber optic adapter 214. Guide 218 allows the optical fiber to be positioned and held within fiber optic groove 216.

[0045] Figure 2E shows a bottom view of another example of the fiber optic adapter of Figure 2C that can be used for color identification of a single optical fiber with the camera system of Figure 1A. The bottom view of fiber optic adapter 214 shows fiber optic groove 216 on the top side of fiber optic adapter 214, but with a mating ridge 220 located partially below the side of fiber optic adapter 214. Mating ridge 220 allows fiber optic adapter 214 to be securely positioned with the spectrophotometer camera shown in the previous example.

[0046] Of course, other configurations of the fiber optic adapter 214 may be utilized to hold the fiber optics in a position that allows for sufficient sampling to allow consistent and accurate identification of the single optical fiber being sampled.

[0047] FIG. 3 shows an example of a camera system with a fiber optic adapter configured for use in color identification of a single optical fiber. The fiber optic adapter 304, a uniformly colored housing, holds the optical fiber 306 in place, ensuring that the optical fiber 306 is always positioned above the 2 mm aperture 302 of the color spectrophotometer camera 102 (e.g., a Konica CM-700d). A holding element (e.g., 206) is used to align the fiber optic adapter 304 with the screw (shown in the previous example). Single-fiber measurements may be taken against the black background of the fiber optic adapter. In this example, a matching algorithm is then used to match the measured values ​​to a lookup table of filled-in reference color measurement data. Both the measurement technique and the matching algorithm are described below with reference to other figures.

[0048] FIG. 4 shows a flowchart of a process of the disclosed subject matter in the examples. The steps of process 400 are executable by a processor, which is operable to identify the color of an optical fiber sample by using one or more algorithms to find a closest match in a database of reference colors. As shown in FIGS. 5 and 6, two example algorithms that can be used in process 400 are described with reference to FIGS. 5 and 6. For example, the processor can access programming code that, when executed by the processor, causes the processor to implement process 400. The programming code is stored in a memory (all of which is shown and described in the examples that follow).

[0049] In step 402, the processor may read a sample color from an optical fiber in the fiber optic cable, where the sample color has a color value, which may be an L*a*b* color value, such as 24.722, 11.480, and 3.862 for red, or similar values ​​for other colors.

[0050] In step 404, the processor may select a color matching algorithm from a plurality of color matching algorithms to identify a color matching algorithm to be used for the single optical fiber.

[0051] In step 406, after selecting a color matching algorithm, the processor may input the color values ​​into the selected color matching algorithm, which processes the input color values ​​against a plurality of reference colors and generates a color match score for each reference color in the plurality of reference colors.

[0052] In step 408, the processor may generate a confidence value based on the ratio of the color match scores of the two closest matched colors. For example, the processor may determine the confidence value using two of the match scores with the closest ratio. The confidence value is an example of how to measure color differences. The generation of the confidence value is described in more detail below.

[0053] In step 410, the processor may select another color matching algorithm by determining whether another color matching algorithm from the plurality of color matching algorithms is available for selection. If the answer to the determination in step 410 is "YES," process 400 may proceed to step 414.

[0054] In step 414, the processor may input the color values ​​into another selected color matching algorithm to generate another color match score using the other selected color matching algorithm. The processor may generate another confidence value using another color match score output from the other selected color matching algorithm. If no other color matching algorithms are available, the processor may evaluate each generated confidence value based on the corresponding color match score from each color matching process.

[0055] If the response to the determination in step 410 is "NO," since no other color matching algorithms are available, the process may proceed to step 412. In step 412, the processor may select the color corresponding to the identified highest confidence value as the color of the optical fibers in the fiber optic cable. The processor may generate an output indicative of the selected color.

[0056] Alternatively, instead of determining a reliability value, the color match score may be normalized between different color matching algorithms. An alternative color matching algorithm may include creating or obtaining a color reference database (e.g., Color[ ]) by measuring samples of optical fiber colors. Once the colors correspond to the reference database, they can be used by the processor. When the optical fiber is detected to be in the proper position, the processor may be operable to perform color measurements on the optical fiber sample under test, either in response to user input or automatically. The processor may identify the closest match color X and corresponding match score_X using a first color matching algorithm (see other figures). The processor may return the color name X and the value of match score_X (e.g., violet and 85). In an alternative example, the processor may identify the closest match color using a second color matching algorithm. In the second color matching algorithm, the processor may identify the closest match color name Y and corresponding match score_Y (e.g., lavender and 83). To identify the color, the processor may evaluate or compare match score_X and match score_Y. Based on the results of the evaluation or comparison, the processor may select a color as the color of the optical fiber and generate an output indicating the selected color. For example, if (match score_X > match score_Y), then selected color = X; otherwise, selected color = Y. Using this logic and the results above, the match score for violet = 85, while the match score for lavender is 83. In this case, the selected color of the optical fiber is violet. In another example operation, Algorithm 1 provides the closest match colors as peach (score: 0.1) and pink (score: 2). For Color Algorithm 1, the confidence score = 2 / 0.1 = 20. For the same optical fiber, Algorithm 2 provides the closest match colors as pink (score: 0.5) and magenta (score: 1). The confidence score = 1 / 0.5 = 2. Therefore, the processor may identify the match color as peach based on the highest confidence score from Algorithm 1.

[0057] Other color matching algorithms may be used, and further examples are described with reference to FIG.

[0058] FIG. 5 illustrates another example of a color matching algorithm according to an embodiment. Process 500 is an example of a color matching algorithm that may be used as a first or second color matching algorithm. A processor may have access to programming code that, when executed by the processor, causes the processor to implement process 500. The programming code is stored in a memory (all of which is shown and described in the examples that follow). When executed by a processor, process 500 enables the processor to identify the closest color match by measuring the Euclidean distance between three coordinates consisting of the L*, a*, and b* values ​​and a reflectance spectral space value S[λ], where S[λ] is the reflectance value at the wavelength λ that returns the maximum spectral response. Of course, other color spaces, such as those described above, may also be used.

[0059] The database 502 of N color entries contains color coordinate values ​​(e.g., L*a*b*) and reflectance spectral spatial values ​​S[λ1-λ N In step 504, the processor i *, a i *, b i * and S[λ i In step 506, the processor may calculate a color match score for each color 1-N in the database, where "i" is the color match score for each corresponding color [1 to N] in the database.

[0060] The processor may calculate the color match score as follows: Color Match Score i = sqrt((a * -a i * ) 2 +(b * -b i * )2 +(S[λ0]-S i [λ0]) 2 ), where the subscript 'i' = 1 to N, where N is the total number of entries in the color reference database, the reflectance spectral space value S[λ0] is the reflectance value at a given wavelength λ0 of the measured color sample, and S i [λ0] is the reflectance spectral value of the reference color from the color entry database.

[0061] In this example, the processor calculates the color matching score i Based on this, the colors from the color entry database 502 may be ranked from closest to least matched, Color[1-N], where the smallest color match score corresponds to the closest match of the measured color sample to the reference color in the color entry database 502 of N color entries. Continuing with this example, the match color may be color X (e.g., match color_X = Color[1]). For example, Color[1] may have a match color score that is 9. The processor may identify another color with the next smallest match color score (i.e., a color match score that is 11). The processor may then use the used smallest match color score and the next smallest match color score to determine the color match score, which is the final score used by the algorithm. For example, the processor may determine the match color score by taking the ratio of the match color scores of the two closest matches of color X or Color[1] in the color entry database 502 of N color entries, where color_Score_X = Score i [2] / Score i [1]

[0062] The algorithm performs the same operation for each color 1-N in a database of N color entries 502. The output of process 500 (the proportion of combined color scores that produce the lowest value) may be provided for use in process 400. For example, the results of the algorithm may be output for further processing by a processor, such as step 412 in process 400.

[0063] 6 shows another example of a color matching algorithm according to an embodiment. A color matching algorithm implemented according to process 600 is operable to enable a processor to match two colors using the reflectance spectra of the two colors. The processor can access programming code that, when executed by the processor, causes the processor to implement process 600. The programming code may be stored in a memory (all of which are shown and described in the examples that follow).

[0064] To resolve amplitude variations, the compared spectra are first normalized, for example, based on the optical fiber adapter. Then, color comparison is performed using three parameters: the Euclidean distance between the two normalized spectra, the mean value of the reflectance spectra, and the amplitude range (max-min) of the reflectance spectra. Details of the normalization and subsequent color matching are shown in Figure 6.

[0065] In this example, S[λ] represents an array of reflectance values ​​at different wavelengths. max(S[λ]) is the maximum reflectance value, and min(S[λ]) is the minimum reflectance value. These two values ​​are at different wavelengths (which is usually not the case unless the spectrum is a flat line). In a measured color sample step 602, the processor may receive a measured color sample of the optical fiber sample under test. The measured color sample may include color space L*, a*, b* values ​​and a reflectance spectrum space value S[λ], where S[λ] is the reflectance value at the wavelength λ that returns the maximum spectral response. Of course, other color spaces may be used, as described above. A reflectance spectrum 604 may be obtained from the color sample measured in step 602. In a normalization step 606, the processor may determine a normalized reflectance S'[λ] of the measured optical fiber sample (as shown in clouds 606 / 614) using the following formula: S'[λ] = (S[λ]-Avg_S) / (Range_S / 2), where Avg_S = average(S[λ]) and Range_S is the difference between the maximum and minimum reflectance values ​​(e.g., Range_S = max(S[λ])-min(S[λ])).

[0066] In normalizing the reference spectrum 614, the processor further determines normalized reflectance values ​​for the color entries in the database of color entries 610. The database of color entries 610 may include measured color samples and matched reference colors. For example, for each color 'j' in the database of color entries 610, the processor executes code to implement a color matching algorithm to determine the normalized reflectance S'j[λ], the average reflectance (Avg_S j ), and reflectance range (Range_S j This determination is similar to that in the reflectance spectrum normalization step 606.

[0067] Given the results of the measured color sample normalization step 606 and the reference spectrum normalization 614, the processor may calculate the color match score for each color 'j' in the database as follows: Score j = ΔS j * (1 + ΔAvg + ΔRange), where ΔS j = Σ(S'[λ] - S' j [λ]) 2 , ΔAvg = abs (Avg_S - Avg_S j ), and ΔRange = abs (Range_S - Range_S j ).

[0068] In response to obtaining the color match, the processor j The processor may rank the colors from closest to least matched (e.g., Color[j]) based on the N color entries. The smallest score corresponds to the closest match. For example, the smallest score may be used for color Y (e.g., match color_Y = Color[3]). In this example, Color[3] may have a match color score of, for example, 8. The processor may identify another color with the next smallest match color score (i.e., a color match score of 10). The processor may then use the smallest match color score and the next smallest match color score to determine a color match score, which is the final score used by the algorithm. For example, the processor may determine the match color score by taking the ratio of the match color scores of the two closest matches of color Y or Color[3] in the database of N color entries 502, where match_Score_Y = Score j [4] / Score j [3].

[0069] The algorithm performs the same operation for each color 1-N in a database of N color entries 610. The output of process 600 (the proportion of combined color scores that produce the lowest value) may be provided for use in process 400. For example, the results of the algorithm may be output for further processing by a processor, such as step 412 in process 400.

[0070] FIG. 7 shows an example of a reference color entry database (L*a*b*). The reference fiber optic color sample database may include a table of optical fiber colors available for use in fiber optic cables. Optical fibers having all or some of the colors may be present in a fiber optic cable. For example, some fiber optic cables may have six optical fibers, while other fiber optic cables may have 12 or 24 optical fibers. By way of example, optical fiber colors used in fiber optic cables may include red, blue, yellow, brown, green, orange, violet, black, rose, aqua, olive, white, lime, tan, magenta, gray, natural, dark green, lavender, purple, sky blue, pink, peach, and saffron. The L*a*b* values ​​in the columns adjacent to the color names are reference values ​​to which measured sample color values ​​are compared for color matching and evaluation.

[0071] 8 illustrates a flowchart of another example process according to an embodiment. In block 802, a processor executing process 800 obtains color values ​​for a fiber optic cable.

[0072] At block 804, the processor is operable to compare the color value of the fiber optic cable to the color value of each reference color of a plurality of reference colors, where each reference color has a unique color value.

[0073] At block 806, the processor generates a color match score for the color value of the fiber optic cable for each of the plurality of reference colors based on the results of the comparison, where the color value of each reference color is different for each reference color and the color match score has a score value. For example, generating the color match score further includes determining a first color match score using a first algorithm and determining a second match score using a second algorithm. In this example, when determining the first color match score, the process may access a database having a plurality of color entries, where each color entry of the plurality of color entries has a color value. For each color entry of the plurality of color entries, a first color match score for the obtained color value of the optical fiber may be determined. Additionally, determining the first color match score may include measuring the Euclidean distance between three coordinates in a color coordinate space for the obtained color values ​​or obtaining a reflectance spectrum value using the obtained color values.

[0074] In block 808, the processor obtains a confidence value for the pair of color match scores that have the closest score values. The process of block 808 may be similar to the process for obtaining confidence values ​​described in reference processes 400 and 500.

[0075] In block 810, the processor may identify one of the reference colors of the plurality of reference colors as the color of the fiber optic cable based on the reliability value. For example, identifying one of the reference colors based on the reliability value may include determining which reference color has the highest reliability value and designating the reference color having the highest reliability value as the color of the fiber optic cable. In another example, identifying may include generating a ratio of two color match scores having match scores closest to the reference color value and designating the ratio as the reliability value. Other technical features will be apparent to those skilled in the art from the following drawings, description, and claims.

[0076] Figure 9 shows a functional block diagram of a color matching system that can be used to implement the examples described with reference to Figures 1A-8. The color matching system 900 includes a spectrophotometer camera 902, a processor 904, an input / output device 906, and a memory 908. The memory 908 includes at least a color entry database 910 and programming code 912.

[0077] Spectrophotometer camera 902 may be camera system 100 operable to extract color data from an optical fiber of a fiber optic cable, such as camera 102 of Figure 1A and capped camera system 300 of Figure 3. As described in the previous example, spectrophotometer camera 902 may include a fiber optic adapter operable to achieve sampling of a single optical fiber, as described in the previous example.

[0078] In this example, the processor 904 is operable to execute programming code 912 to provide the optical fiber color identification process described with reference to the examples of FIGS. 1A-8. For example, the memory 908 may store programming code 912 that, when executed by the processor 904, implements processes 400, 500, 600, and 800. The memory 908 may also store a color entry database 910, which is a database of N color entries (e.g., the colors shown in the example of FIG. 7). Each color entry in the color entry database 910 may include a color space value for each color entry, such as an L*a*b* color space value and a reflectance spectrum space value. Each of the N color entries corresponds to a color of an optical fiber used in one or more configurations of the optical fiber cable. The optical fiber cable may have, for example, 6, 12, or 24 optical fibers. The programming code 912 may further include multiple types of color matching algorithms described with reference to earlier examples (e.g., the examples of FIGS. 5 and 6).

[0079] The input / output device 906 may be a touch panel display of a mobile device, tablet computing device, laptop computer, dedicated computing device, etc. Alternatively, the input / output device 906 may be a display device coupled to a keyboard, touch panel, mouse, etc. The processor is operable to receive configuration and parameter changes in the color entry database 910 or color matching algorithm via the input / output device 906.

[0080] 1A to 5 may include various hardware elements, software elements, or a combination thereof. Examples of hardware elements may include structural parts, logic devices, components, processors, microprocessors, circuits, processors, circuit elements (e.g., transistors, resistors, capacitors, inductors, etc.), integrated circuits, application specific integrated circuits (ASICs), programmable logic devices (PLDs), digital signal processors (DSPs), field programmable gate arrays (FPGAs), storage means, logic gates, registers, semiconductor devices, chips, microchips, chipsets, etc. Examples of software components may include software components, programs, apps, computer programs, applications, system programs, software development programs, device programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, processes, software interfaces, application program interfaces (APIs), instruction sets, operation codes, computer code, code segments, computer code segments, words, values, codes, or any combination thereof.

[0081] Disclosed herein is a new and unique technique for improved detection of cables and cable joints. The scope of the disclosure is not limited by the specific examples described herein. Indeed, various other examples and modifications of the disclosure, in addition to those described herein, will be apparent to those skilled in the art from the foregoing description and drawings.

[0082] Accordingly, such other examples and modifications are intended to be within the scope of the present disclosure. Moreover, while the present disclosure has been described herein in the context of particular embodiments for particular purposes in particular environments, those skilled in the art will recognize that its usefulness is not limited thereto, and that the present disclosure may be beneficially implemented in any number of environments for any number of purposes. Therefore, the following claims should be construed in light of the full breadth and spirit of the present disclosure as described herein.

Claims

1. obtaining, by a processor, color values ​​of optical fibers in the optical fiber cable from a spectrophotometer camera; comparing the color value of the optical fiber to a color value of each reference color of a plurality of reference colors, each reference color having a unique color value; generating a color match score for the color value of the optical fiber for each of the plurality of reference colors based on the comparison, the color value of each reference color being different for each reference color, and the color match score having a score value; obtaining a confidence value for the pair of color match scores with the closest score value; and identifying one of the plurality of reference colors as the color of the optical fiber based on the reliability value. method.

2. generating the color match score comprises: identifying a first color match score using a first algorithm; and determining a second color match score using a second algorithm. The method of claim 1.

3. Determining the first color match score comprises: identifying a first color match score for the obtained color value of the optical fiber with each color entry of a plurality of color entries stored in a database; The method of claim 2.

4. Determining the first color match score comprises: For the obtained color values, measuring the Euclidean distance between three coordinates in the color coordinate space; The method of claim 2.

5. Determining the second color match score comprises: obtaining a reflectance spectrum value using the obtained color value; The method of claim 2.

6. identifying one of the plurality of reference colors as the color of the optical fiber based on the reliability value; determining which reference color has the greatest confidence value; and designating the reference color having the greatest confidence value as the color of the fiber optic cable. The method of claim 1.

7. generating a ratio of the two color match scores that have the match score closest to the reference color value; and designating the ratio as the reliability value. The method of claim 1.

8. a spectrophotometer camera; a fiber optic adapter operably holding a single optical fiber of a fiber optic cable in the field of view of the spectrophotometer camera; a processor; a memory having instructions stored therein; The instructions, when executed by the processor, cause the processor to: obtaining a color value of a single optical fiber in the fiber optic cable from the spectrophotometer camera; comparing the color value of the single optical fiber with the color value of each reference color of a plurality of reference colors, each reference color having a unique color value; generating a color match score for the acquired color value of the single optical fiber for each reference color of the plurality of reference colors based on the comparison, the color value of each reference color being different for each reference color, and the color match score having a score value; obtaining a confidence value for the pair of color match scores with the closest score value; identifying one of the plurality of reference colors as the color of the single optical fiber based on the reliability value. system.

9. When generating the color match score, the instructions further cause the processor to: identifying a first color match score using a first algorithm; determining a second color match score using a second algorithm. The system of claim 8.

10. When determining the first color match score, the instructions further cause the processor to: accessing a database having a plurality of color entries, each color entry of the plurality of color entries having a corresponding color value; and determining a first color match score for the obtained color value of the single optical fiber using each color entry of the plurality of color entries. The system of claim 9.

11. When determining the first color match score, the instructions further cause the processor to: For the obtained color values, measuring the Euclidean distance between three coordinates in the color coordinate space; The system of claim 9.

12. When determining the second color match score, the instructions further cause the processor to: obtaining a reflectance spectrum value using the obtained color value; The system of claim 9.

13. When recognizing the one reference color based on the reliability value, the instructions further cause the processor to: determining which reference color has the greatest confidence value; designating the reference color having the greatest confidence value as the color of the fiber optic cable. The system of claim 8.

14. The instructions further cause the processor to: generating a ratio of the two color match scores that have the match score closest to the reference color value; designating the ratio as the reliability value. The system of claim 8.

15. The method comprises instructions that, when executed by a computer, cause the computer to perform each step of the method of claim 1. Computer program.

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