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Adaptability Study of Color Sorter Vision Recognition System in Complex Material Environments
2025-08-27Complex material environments—such as high humidity, dust, and mixed impurities—pose major challenges to the Color Sorter vision recognition system, which is the core of Optical Sorter and Color Sorting Machine operations. To address this, targeted optimizations have been made for specialized models like Rice Color Sorter, Grain Color Sorter, and Tea Color Sorter.
First, for dusty environments (common in Grain Color Sorter use), the vision system is equipped with anti-dust glass and automatic air-blowing cleaning modules. This prevents dust from adhering to lenses, ensuring the Optical Sorter maintains clear imaging—reducing mis-sorting caused by blurred visuals by 50%. In high-humidity scenarios (e.g., Tea Color Sorter processing fresh tea), the system uses waterproof cameras and dehumidifying components to avoid lens fogging, keeping sorting accuracy above 99.6%.
Second, for materials with mixed shapes and colors (e.g., Bean Color Sorter handling mixed bean varieties), the vision system adopts multi-angle shooting (3-5 cameras) and AI-based feature extraction. It can distinguish between target beans and impurities (like small stones or broken beans) even when they have similar colors, outperforming traditional single-angle vision systems. For Rice Color Sorter dealing with rice mixed with discolored grains and hulls, the system uses high dynamic range (HDR) imaging to capture subtle brightness differences, improving defect recognition rates by 35%.
Additionally, the vision system of modern Color Sorter supports real-time parameter adjustment. When material characteristics change (e.g., moisture content fluctuations in grains), the system automatically calibrates exposure and color thresholds—ensuring stable performance without manual intervention. This adaptability makes it more reliable than fixed-parameter vision systems in complex environments.
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