In vehicles, connectors are used to form a continuous electronic signal by connecting wire harnesses to a power source or circuit within vehicles. In the simplest terms, connectors consist of jacks that are joined together to relay an electronic signal. A male-ended plastic housing fits into a snap-mounted female-ended chassis mounting. Because the pair can be connected or disconnected easily, it is important to verify full insertion, which ensures proper functioning of electrical components within the vehicle.
Edge learning can determine whether the jacks are correctly seated. In addition, it can be retrained to classify connectors by different shapes (circular, header, pin, crimped, multitap), materials, quality, and dimensions depending on the application.
Many printed circuit boards (PCBs) include LED indicator lights to show status. Typically, the status is one of three conditions: power on (PWR), transmit (TX), or off (OFF).
Using traditional machine vision, the standard way to determine whether indicators show a PWR, TX, or OFF condition is with a pixel count tool. This involves setting thresholds for brightness at specific locations for each condition, a complex process that requires extensive programming expertise.
Edge learning, however, can be trained on a small set of labeled images of the PWR, TX, and OFF conditions. After this brief training, edge learning reliably sorts the PCBs into the three different states.
Test tubes are commonplace in lab diagnostic environments. A single tube can convey a considerable amount of information – from a barcode adhered to the side to the color of the cap – that helps clinicians to properly process test samples. Cap color, in particular, is useful in distinguishing one sample from the next. For instance, the cap color often denotes what additives have been mixed with the sample to stabilize and preserve the specimen prior to testing.
Edge learning can not only detect the presence/absence of a cap on a test tube to ensure it’s properly sealed, but can go one step further and identify the color of the cap. This enables clinicians to more efficiently run lab diagnostics and ensure samples are correctly processed.
In the medical and pharmaceutical field, glass vials are often filled with medication to a predetermined level. Before they are capped, the fill level must be confirmed to be within proper tolerances. The transparent and reflective nature of both the glass vial and its contents makes it difficult for traditional machine vision to consistently detect the fill level.
Edge learning can be deployed to identify the level, without getting confused by reflections, refraction, or other disorienting variables within the inspection image. Fills that are too high or too low are classified as NG (failing), while only those within the proper tolerances are classified as OK (passing).
Before distribution, bottles of soft drinks and juices are filled and sealed with a screw cap. If the rotary capper misthreads the cap, or it gets damaged during the capping process, this can leave a gap that allows for contamination or leakage. Both the speed and the wide range of ways in which a cap may be almost, but not quite sealed, make this a challenging application for traditional machine vision to automate.
Edge learning can be given a set of images labeled as either good (properly sealed caps) or bad (caps with slight, almost imperceptible, gaps). While maintaining line speeds, edge learning identifies fully sealed caps and categorizes them as OK. All other caps that do not meet the quality threshold are classified as NG.