Sony Semiconductor Solutions Group

Optimize Flow and Minimize Friction with Vision AI

Vision AI has the potential to enhance logistics efficiency, transparency, and cost-effectiveness, shaping a more optimized future for businesses. AITRIOS combines powerful edge AI devices with user-friendly, low-code AI tools, empowering any team to solve logistics challenges by quickly training and deploying vision AI models – no data engineering expertise required.

Inventory Tracking

Transform your inventory tracking process with vision AI. Sony’s IMX500 intelligent vision sensors are the eyes of your vision AI system, collecting all the visual data and integrating it with existing inventory management systems, providing real-time updates that keep teams always informed. Automate tedious manual processes to not only save time but also dramatically reduce errors and gain real-time insights into inventory. With AITRIOS, teams can build custom vision AI models that alert when stock is running low, monitor shelf space, or accurately count inventory to prevent costly headaches like stock outs and overstocks while also streamline processes.

Yard Management

Vision AI streamlines check-in processes, improves traffic flow, and enhances vehicle mobility in yard management. Sony's IMX500 intelligent vision sensors are at the heart of this transformation, capturing detailed information about trucks, chassis, trailers, containers, and available spaces. This boost in visibility and control is a game-changer for logistics operations. With AITRIOS's no-code AI tools, teams can quickly build custom vision AI models tailored to their specific needs. These models seamlessly integrate visual information with existing yard management systems to help teams see significant improvements in throughput, faster check-in times, and more accurate container tracking. All of this adds up to smoother, uninterrupted operations.

Operations

Teams can optimize various aspects of their operations with vision AI to improve picker efficiencies, monitor worker safety, optimize space utilization, and provide real-time data and images to 3PL customers. Sony's IMX500, intelligent vision sensors, monitor the aspects of the warehouse and logistics operations that are most important to the team, leading to increased productivity, reduced costs, and enhanced customer satisfaction.

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Case Study: AITRIOS enhances a Warehouse Management System

Explore how an ecommerce company turned to an AI solution that relied on computer vision and machine learning for automated cycle counts to overcome the growing pains associated with scaling their business.

Latest Updates

Sony Semiconductor Solutions Corporation Partners with Ultralytics to Enhance Edge AI Capabilities with Ultralytics YOLOv8 Integration

Sony Semiconductor Solutions Corporation (SSS) is excited to announce a new partnership with Ultralytics, a leader in Vision AI technology and the creator of the renowned YOLO (You Only Look Once) model series. This collaboration enables Ultralytics YOLOv8 to integrate seamlessly with the IMX500 Intelligent Vision Sensor from SSS, delivering advanced, real-time object detection capabilities directly at the edge.

Local Studio, the AI Training Tool for AITRIOS, is now compatible with the Raspberry Pi AI camera

Local Studio was designed for industrial-grade applications and can be used in a local network environment, especially convenient for rapid prototyping. With this tool, users will be able to develop AI models without having prior, specialized AI knowledge or education, making it a quick-start way to explore the new Raspberry Pi AI Camera.

Open Source Ecosystem Conference SSS will exhibit at Open Source Summit Japan & AI_dev

Sony Semiconductor Solutions Corporation (SSS) will be taking part as a platinum sponsor of the "Open Source Summit Japan" and "AI_dev: Open Source GenAI & ML Summit Japan" in Toranomon Hills Forum from October 28-29th, 2024. We will exhibit AITRIOS™, Sony’s edge AI sensing platform, as well as our recently-announced AI camera jointly developed with Raspberry Pi.