Deep Learning & Edge Learning

logistics automation | Deep learning & edge learning

Help automate and scale up complex checks.

Deep learning technology – using artificial intelligence – learns to find patterns and anomalies in sample images.

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Deep learning technology – using artificial intelligence – learns to find patterns and anomalies in sample images. This helps automate and scale up complex checks that usually need human inspectors.

With its proven algorithms, deep learning technology is a game-changer in automating factories, particularly machine vision. Moreover, its intuitive and user-friendly interface makes training the system a breeze, ensuring ease of use without compromising performance.

Tasks that were previously too costly, too time-consuming, or too fast for humans or traditional machine vision to handle reliably can be done quickly and accurately, leading to a significant boost in operational efficiency.

Our deep learning tools on the VisionPro ViDi and In-Sight platforms are designed to tackle even the most challenging tasks. They excel in part location, assembly verification, defect detection and segmentation, material classification, and Optical Character Recognition applications, making them a versatile solution for various industrial needs. With their intuitive and user-friendly interface, training the system is a breeze, ensuring ease of use without compromising performance. This is your key to unlocking the full potential of deep learning in your operations.

We focus on the following types of tools:

Machine learning, a key component of our solution, streamlines warehouse operations by automating tasks prone to human error. This includes automating routine tasks such as picking, sorting, and palletising, increasing efficiency and reducing delays or lost shipments.

Edge learning is an efficient subset of AI where processing occurs on-device. It requires minimal training data and enables quick learning, making it accessible for beginners and automation task experts.

Choosing between Edge Learning & Deep Learning

Edge learning focuses on quick deployment and efficiency for simpler tasks, requiring minimal images for training, while deep learning suits complex applications with detailed image sets and demands more computational power and training time.

Advantages of using Edge Learning in Logistics:

Quick Deployment:

Edge learning allows for rapid deployment of AI solutions, enabling logistics companies to implement new technologies efficiently and adjust to changing demands swiftly.

Efficiency:

Edge learning tools can be trained in minutes with as few as five to ten images. This training speed enables logistics companies to streamline processes and improve operational efficiency.

Minimal Training Data:

Edge learning requires fewer training images than deep learning, making it easier for logistics companies to gather and label the necessary data for training their AI models.

Embedded Vision Systems:

Optimising edge learning for embedded vision systems allows for real-time data processing at the edge, reducing latency and improving responsiveness in logistics operations.

Cost-Effective:

Edge learning’s efficiency and speed translate to cost savings for logistics companies, reducing the time and resources required for setup, installation and commissioning of the AI -based solution.

Adaptability:

With edge learning, logistics companies can easily adjust their AI solutions to new scenarios or conditions, making their operations more adaptable to evolving industry requirements.

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