Detection

Detection engines are responsible for locating objects in the 3D scene captured by the Pickit camera. Different applications require different detection engines depending on the shape and arrangement of the objects. In the Applications section, you can learn more about how they are supported by Pickit, and which detection engines are available for each application.

Pickit has two general detection engines, each optimized for a different type of shape.

• Teach is the most versatile engine, and well suited for most shapes of known fixed sizes, both simple and complex. It is the recommended detection engine for most applications, ranging from complex shapes to simple shapes (like cylinders) of known dimensions.

• Flex is meant for detecting simple geometric shapes in 3D (cylinders and spheres) and 2D (squares, rectangles, circles and ellipses). It can detect instances of the same shape with similar or different sizes. It is recommended for cylinders of varying sizes, but for cylinders of fixed sizes prefer Teach.

Pickit also offers detection engines optimized for specific part shapes and arrangements:

• DeepAL, which is specially designed for depalletization applications, where the parts to pick are mostly flat and stacked in layers in semi-structured patterns. Typical applications include the depalletization of boxes, totes, buckets.

• Pattern, which is similar to Flex. It looks for 2D shapes (rectangles, squares, circles and ellipses) of known fixed size, and is especially useful for detecting parts that are aligned and touching.

• Bags, which is specially designed for detecting bags on a pallet.

• Hole, for detecting circular holes of known size on a surface.