Inspecting Automotive Instrument Clusters using IMAQ Vision


For automotive component manufacturing engineers who build test systems for instrument clusters, National Instrument’s imaging products provide tools for building low-cost machine vision inspection systems. Unlike other vendor’s solutions, National Instruments image acquisition hardware and image processing software can run in a deterministic and reliable software environment that easily integrates with other test equipment and measurement types.



Automotive instrument cluster suppliers are challenged with creating systems for testing instrument displays. Numerous types of automotive instrument clusters need to be tested, including large gauges for speed and RPM, smaller gauges for gas and temperature, status indicators for turn signals and system faults, digital and analog odometers, and digital status displays. Instrument clusters must be tested for functionality and interoperability under a variety of of test conditions. New models with unique features are being introduced every year, so test engineers must create test systems that are adaptable to new requirements.

Figure 1 A typical instrument cluster

Example Inspection application

The image shown in Figure 1 shows a typical test instrument cluster test article. An inspection test sequence for this model would:

  1. Create a coordinate reference system based on the alignment of the cluster in the nest.
  2. Verify the correct placement of the cluster in it's nest.
  3. Read the speedometer.
  4. Read the tachometer.
  5. Check for status of the icons (ON/OFF).

Example Pseudocode

The following steps represent the pseudcode for executing the inspection tasks. The inspection tasks as performed in the sample code are shown in Figure 2. The numbers in blue represent the test steps outlined.

  1. Find the location and orientation of the odometer display. Use edge detection tools to find the left edge (1) and the lower edge (2) of the odometer display area. Depending on the cluster, pattern matching can be used in place of using the edge detection tools. The choice will depend on the type of fiducials available for identification and location.
  2. Create a coordinate reference frame based on step 1. This allows subsequent images to relocate the defined Regions Of Interest at the correct locations.
  3. Check for the presence of the seatbelt icon (3) and the PS icon (4) using Pattern Matching. Check for their orientation and the distance between them to ensure correct placement of the cluster in its nest.
  4. Read the speedometer gauge needle. Find both the edges (5) and (6). Compute the bisector of the two edges to find the center of the needle (7) (Figure 3). The inspection is based on the coordinate reference frame created earlier in step 2. Find the angle of the bisector with respect to the start position of the gauge.
  5. Read the tachometer (8, 9, 10) in the same way as in step 4.
  6. Check for the status of the rest of the icons (ON/OFF) (11, 12, 13, 14) by comparing the mean intensity of these against expected values. The location of these is based on the coordinate reference frame created earlier in step 2.

Figure 2. Screenshot of the test sequence on the cluster image. 

Figure 3. Closeup of the measurement to find the center of the needle

Figure 4 shows the state flow diagram for inspecting a series of instrument clusters.

Figure 4. Pseudocode State Diagram 


There are a few issues that you should be aware of when setting up a system for cluster inspection. Understanding these issues can help you optimize the performance of your test system.

  1. As can be seen in the cluster image, the markings in the gauges are non-linear. The angle between the "0" and "20" kph is not the same as the angle between "20" and "40" kph. The speedometer measurement algorithm should measure the angle of the needle with respect to the zero mark and then translate the measured angle to the correct measurement using a lookup table or other representation of the expected speedometer behavior
  2. When a cluster is initially powered ON, the gauge needles should point to the zero marks. Needle guages may have an offset that should be reported as a defect and/or included in the instrument reading measurement algorithm.
  3. In the example code and the technique described above for finding the needle (5, 6, 7) in Figure 2, the "Find Straight Edge" tool is used to find the edges of the needle and then to compute the center of the needle. It is not always possible to create an annular search area that would allow only the edges of the needle to be found (this is the reason that the width of the annular search region here is narrow). Another option is to use a freehand drawn line drawn in parallel to an annular region. The two edges of the needle can be found as before, and the mid-point between these edge points lies on the center line of the needle. Together with the pivot point of the needle, this point can be used to determine the needle's angle.
  4. The cluster inspection speed depends on several factors, including
    • number of inspection tasks, and
    • the algorithm used for each of the tasks

There are also some techniques that leverage prior knowledge of the test article geometry that can be used to improve execution time. For example, in the test step 3 above, the search Region Of Interest (ROI) for the icons could be defined with a very narrow tolerance, which would allow the pattern matching to run more quickly.


When creating an instrument cluster inspection system for continuous measurements, sometimes the inspection cycle time is a critical parameter, i.e., the inspections needs to be accomplished within a specific time. The best solution for this requirement is to use a system based on the LabVIEW Real-TIme execution environment. With LabVIEW RT, and the Vision Development Module for LabVIEW, you have all the tools you need to develop a complete machine vision application on a reliable, embedded platform. LabVIEW RT provides real-time programming and execution capabilities and the Vision Development Module provides the image acquisition, processing and analysis functions. Using this platform would allow the system to run in a time-bounded manner. For implementation details and limitations, please refer to the IMAQ Vision for LabVIEW User Manual.


Recent advances in Automotive test applications have required tighter integration with other measurement and automation devices. National Instruments IMAQ hardware is designed to integrate seamlessly with NI data acquisition, CAN and motion control hardware. You can use the real-time system integration (RTSI) bus for PCI or the PXI trigger bus to distribute timing and triggering signals among National Instruments I/O devices.