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  • Detecting Dark Objects

    Q4X sensor detects very dark parts

    Dark objects, such as solar wafers or automotive parts, absorb a large amount of light, especially red light. Due to the low reflectivity of these targets, it can be challenging for LED- and laser-based sensors to reliably detect the presence of very dark objects—especially against a similarly dark background.   

    In spite of the challenges, there are both photoelectric and laser sensors that can reliably solve these applications. Keep reading to learn how to identify a solution for dark object detection. 

    Challenges of Detecting Dark Objects

    Q4X sensor detects dark features on an equally dark background

    There are several options for detecting dark objects. A through-beam or retro-reflective photoelectric sensor is an obvious choice since the sensor can easily detect when a dark object passes between the emitter and reciever unit, or when the beam of light between the emitter and a reflector is interrupted. However, in many applications, mounting devices on both sides of the application is impractical, if not impossible.   

    In these cases, a diffuse sensor (emitter and receiver in the same housing) is required.  However, diffuse sensors rely on the target to act as a reflector, and targets must return enough light back to the sensor to verify the object's presence. If a target aborbs the majority of the light, as in the case of dark objects, the sensor may fail to reliably detect the target.  To solve these challenges, a sensor with high excess gain, or blue light instead of red, can help ensure reliable detection. 

    In many of these applications, not only must the sensor detect the dark object, but it must do so against an equally dark background (for example, detecting dark parts on a dark auto body panel). The lack of contrast between target and background is also challenging for many photoelectric sensors to detect.  For these applications, a distance-based laser sensor is a must.  Keep reading to learn why. 

    3 Solutions to Reliably Detect Dark Objects

    The Q5X sensor detects black components on a black seat
    High Excess Gain

    Excess gain is an important metric for any application, but it is especially important when detecting dark objects. Excess gain is a measure of the minumum light energy needed for reliable sensor operation. A high excess gain helps the sensor overcome a weaker signal reflected by a dark object.  

    The amount of excess gain you need, over and above what you might need for a "typical" target, will depend on how dark your target is as well as the environment of your application.  Pristine environments with little dust will require less excess gain than a dirty environment with debris that can cloud the sensor lens and further reduce signal strength. 

    Fortunately, determining the amount of gain you need doesn't have to be a manual process. Smart sensors can recognize if very little light is returning to the receiver and automatically adjust exposure settings for additional gain and a stronger signal.

    Distance-Based Detection

    Sensors that rely solely on contrast will struggle to detect very dark objects against a similarly dark background; however, these applications are common, especially in the automotive industry.  For example, in the application pictured to the right, verifying that foam and other components are present on automotive door panels is extremely important.  If any part is missing, the quality of the final door assembly is adversely affected. 

    Since door panels are often dark colors such as black or gray, and the foam used to eliminate rattles and provide stiffness is generally black, it can be difficult for standard sensors to differentiate between foam presence and absence due to poor contrast.  A laser measurement sensor can reliably detect these parts by detecting changes in distance instead of color. 

    The Q4X sensor detects dark parts on a dark background in an automotive application
    Solar cells and other very dark objects absorb light and are difficult to detect
    Blue LED Light

    Some targets are designed specifically to absorb red light. For example, solar wafers are designed to absorb as much sunlight as possible and are covered in anti-reflective coating. This makes solar wafers very difficult for traditional photoelectric sensors to detect. 

    In these applications, a photoelectric sensor with a blue LED light can be a cost-effective solution. Blue light is not absorbed by dark objects to the same extent as red light. Since more light is remitted back to the sensor, a sensor with blue light will be able to detect extremely dark objects with greater reliability.

    Featured Products

    Q4X Sensor
    Q4X Series

    Laser Distance Sensor, 25-610 mm

    坚固耐用的多功能型激光测距传感器性能卓越,能够检测出亚毫米级的距离变化。

    • 开关量,模拟量(0-10 V或4-20 mA),透明物体检测,可提供IO-Link型号
    • 可靠的检测范围,近至25毫米,远至610毫米
    • 根据距离检测各种各样的目标颜色、材质和表面
    • 双示教模式(强度+距离),是防错应用的理想选择,可实现透明物体检测,无需反射板
    • 坚固耐用的FDA级不锈钢外壳,防护等级达IP69K
    Q5X Series

    Laser Distance Sensor, 9.5 cm - 2 m Range

    Q5X 背景抑制激光器功能强大,是一款善于解决问题的传感器,内置在经济高效、行业标准矩形外壳。

    • 量程为 9.5 厘米(4 英寸)至 2 米(6 英尺 6 英寸)
    • 由于过量增益极高,传感器能够可靠检测极暗的物体(反射率 <_6 的黑色物体="的黑色物体" li="li">
    • 可靠检测黑色背景下的黑色目标、闪亮金属背景下的黑色目标、透明和反光物体、多色包装以及所有颜色的目标
    • 双重示教模式,可测量距离和光强,能解决极具挑战性的应用
    Q5X sensor
    VS8 Sensor
    VS8 Series

    Photoelectric Sensor with Blue LED

    • Miniature sensor for installation in the smallest of spaces
    • Red laser models provide bright, precise laser light spot for optimum small part detection
    • Models with a blue LED reliably detect challenging targets, including dark, reflective, and transparent objects without requiring a reflector
    • High switching frequency for detection in even the fastest processes
    • User-friendly operation using electronic push button or remote input provides reliable and precise detection
    • Red laser, Red LED, and Blue LED types available to match sensing beam to application
    • Robust, glass-fiber-reinforced plastic housing
    • PNP or NPN output, depending on model
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