The inspector combines traditional machine vision methods and artificial intelligence algorithms to analyze the rice. First, the traditional visual method is used to segment the rice grains in the video frame, and then the artificial intelligence algorithm is used to identify the attributes of the segmented rice grains and judge Whether there are insect-eaten, sprouting, mildew and other problems. At the same time, two high-resolution cameras were used to photograph the front and back of the rice, and the properties of the two sides were analyzed. Through the registration algorithm, the front and back of the rice are registered one by one, and their respective attributes are combined to obtain the attributes of a complete rice grain.

Inspection details

The analyzer combines traditional machine vision methods and artificial intelligence algorithms to analyze rice. First, traditional visual methods are used to segment the rice grains in the video frame, and then artificial intelligence algorithms are used to identify the attributes of the segmented rice grains and judge Whether there are insect-eaten, sprouting, mildew and other problems. At the same time, two high-resolution cameras were used to photograph the front and back of the rice, and the properties of the two sides were analyzed. Through the registration algorithm, the front and back of the rice are registered one by one, and their respective attributes are combined to obtain the attributes of a complete rice grain.

Key technology

1. Automatic binarization: Use deep neural network to segment the foreground and background of the image. Compared with the traditional binarization method, it can be applied to a variety of lighting conditions, and the edge segmentation of rice is smoother, fast and robust High advantages.

2. Adhesive rice segmentation algorithm: The method based on connected domains cannot segment the adhered rice. The deep neural network is used to segment the adhered rice at an instance level, which can reach a speed of 1000fps and can process the adhered rice in real time.

3. Rice attribute recognition algorithm: adopts a lightweight neural network and integrates a semi-supervised learning method. The model can be iteratively optimized only by marking a small amount of data. It has the advantages of high accuracy, fast speed, and convenient deployment.

Range of application

KVS-G series grain quality analyzer is composed of visual system, software system and other module structures. When the grain enters the field of view of the camera, the grain is photographed, and the characteristics of a complete grain are obtained through the registration algorithm. Attribute identification to determine whether there are problems such as disease spots, mold growth, budding, damage, and insect erosion. It has a wide range of applications and is of great significance to the sorting and quality improvement of grains.

** In addition to rice, agricultural products such as melon seeds, pine nuts, almonds, coffee beans, and betel nuts that can be placed stably can be analyzed by our analyzer.