Seep Perdition using Image Recognition based on Deep Learning in Python

Abstract:

Most commercial optical sorting systems are designed to achieve high throughput, so they use a naive low latency image processing for object identification. These naïve low-latency algorithms have difficulty in accurately identifying objects with various shapes, textures, sizes, and colors, so the purity of sorted objects is degraded. Current deep learning technology enables robust image detection and classification, but its inference latency requires several milliseconds; thus, deep learning cannot be directly applied to such real-time high throughput applications. We therefore developed a super-high purity seed sorting system that uses a low-latency image-recognition based on a deep neural network and removes the seeds of noxious weeds from mixed seed product at high throughput with accuracy.