While many manufacturers are already employing automated inspection systems that incorporate vision components to increase the efficiency and quality of the processes and products, we may have only hit the tip of the iceberg when it comes to the potential of machine vision. By that, I mean that the machine vision market will expand to encompass everything from harvesting crops and minerals to delivering products directly to consumer’s doors. In a future that demands increased efficiency, such machine vision systems will be used in systems that automate entire production processes, effectively eliminating the manpower and errors that this entails.
Here are five examples of current machine vision applications that are already performing automated tasks today that represent the "automatization" of tasks currently delegated to human operators.
1. 3D mapping
Michael Nielsen, and his colleagues at the Danish Technological Institute used a Trimble GPS, a laser rangefinder from SICK, a stereo camera and a tilt sensor from VectorNav Technologies mounted on a utility vehicle equipped with a Halogen flood light and a custom made Xenon strobe. After scanning rows of peach trees, 3D reconstruction of an orchard was performed by using tilt sensor corrected GPS positions interpolated through encoder counts. Click here to read more.
2. Automated harvesting
Guo Feng and his colleagues at the Shanghai Jiao Tong University have developed an imaging system mounted onto a robot designed to harvest strawberries. A 640 x 480 DXC-151A from Sony mounted on the top of robot frame captures images of 8-10 strawberries. Another camera, a 640 x 480 EC-202 II from Elmo was installed on the end effector of the robot to image one or two strawberries. While the Sony camera localized the fruit, the Elmo camera captures images of strawberries at a higher resolution. Images from both these cameras were then captured by an FDM-PCI MULTI frame grabber from Photron interfaced to a PC. A ring-shaped fluorescent lamp installed around the local camera provided the stable lighting required for fruit location. Click here to read more.
3. Crop grading
Olivier Kleynen, and at the Universitaire des Sciences Agronomiques de Gembloux design a system to detect defects on harvested apples. The system used a MultiSpec Agro-Imager from Optical Insights that incorporated four interference band-pass filters from Melles Griot was coupled to a CV-M4CL 1280 x 1024 pixel monochrome digital camera from JAI. In operation, the Multi-Spec Agro-Imager projects on a single array CCD sensor four images of the same object corresponding to four different spectral bands. Equipped with a Cinegon lens from Schneider Optics, camera images were then acquired using a Grablink Value Camera Link frame grabber from Euresys. Hyper-spectral images were then used to determine the quality of the fruit. Click here to read more.
Page 1 | Page 2
