期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 3, 期 4, 页码 3035-3042出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2018.2849513
关键词
Agricultural automation; deep learning in robotics and automation; computer vision for automation
类别
资金
- National Research Foundation of Korea - South Korea government [2011-0030075]
- Industrial Technology Innovation Program - Ministry Of Trade, Industry & Energy (MI, South Korea) [10048358]
- Korea Evaluation Institute of Industrial Technology (KEIT) [10048358] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2011-0030075] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Most commercial optical sorting systems are designed to achieve high throughput, so they use a naive low-latency image processing for object identification. These naive 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. The proposed system partitions the detection task into localization and classification, and applies batch inference only once strategy; it achieved 500-fps throughput image-recognition including detection and tracking. Based on the classified and tracked results, air ejectors expel the unwanted seeds. This proposed system eliminates almost the whole weeds with small loss of desired seeds, and is superior to current commercial optical sorting systems.
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