TI’s AM6xA Processors are designed for vision applications requiring intensive image analytics. Retail checkout and scanner applications like item recognition, barcode scanning and decoding, and theft detection benefit from imaging and vision analytics to improve accuracy, speed, and generality to new environments. This application note analyzes a retail checkout demo application, which uses a raw camera sensor and runs a gstreamer-based application with deep learning for object detection, on the AM62A's heterogeneous architecture. The core load across the AM62A processor is used to select a cost-optimized version of the system-on-chip (SoC) and the application's power usage shows SoC consumpion is under 2 Watts active power.
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Smart camera applications in retail sectors are increasing in popularity given the rich information content in imagery as well as the improving capability of processing that data at the edge. Use-cases like checkout scanners, barcode images, asset and people tracking, theft detection, and more are helping to automate, simplify, and streamline customers’ experiences.
Although vision is an intuitive concept for humans, computer vision is challenging. Imagery is information-dense and visual patterns can occur in many shapes, sizes, and contexts. Conventional computer vision using filters, transforms, and specialized algorithms are effective; However, there algorithms are often difficult to accelerate in their entirety and can require careful tuning to the environment and specifics of the task. Machine learning and deep learning approaches to image processing are much more generalized and often boast higher accuracy. While these learning-based algorithms (AI) are often more computationally complex, they are also easier to accelerate given their strong reliance on matrix math.
Processing imagery with deep learning and AI in the cloud be done easily, yet comes at more significant and recurring cost than processing locally. Reactive applications like checkouts would be slow and frustrating for customers due to network latency. Security applications can cause privacy concerns. Furthermore, as the solutions scale, the associated cloud costs will scale similarly. Processing imagery locally on the device that captures video solves these issues, but requires a processor that matches requirements for cost, power, and performance.