The invention discloses a fruit quality detection system and method based on depth learning. The fruit quality detection system and method based on depth learning mainly judge the fruit quality according to the color, texture, shape and size of the fruit surface to complete the classification. The whole detection system is composed of a fruit transmission system, a computer vision recognition system and a classification system. Compared with a traditional fruit detection system, the system has the distinct characteristics of deep learning, and the characteristics are mainly reflected in the fruit image processing. Through the detailed analysis of a network structure of a convolution neural network algorithm and the training process of the convolution neural network, a fruit image recognition system based on the convolution neural network is constructed. Compared with a general neural network algorithm, the fruit image recognition system has the characteristics of simple structure, lesstraining parameters, high adaptability and the like.

Fruit quality detection system and method based on depth learning

The invention discloses a micro part quality detection system based on a convolutional neural network. The micro part quality detection system comprises: A, collecting the surface image information ofa micro part by an image acquisition module formed by microscopic vision; B, detecting the image collected by the microscopic vision by using a convolutional neural network model, and classifying thedetected defect images; C, transmitting the classifying result into a main controller, and sending a control signal to a terminal actuator; and D, carrying out picking and classifying on the corresponding micro part by the terminal mechanical arm actuator according to the control signal transmitted by the controller so as to convey the part into the corresponding receiving box, such that the whole system completes the detection and defect classification on the surface quality of the micro part. According to the present invention, the system can effectively used for the detection of micro parts, and can improve the automation degree and the efficiency of detection, and reduce the influence of human factors on the detection process and the labor intensity of workers.

Micro part quality detection system based on convolutional neural network

The invention puts forward a potato surface defect detection method based on a convolutional neural network. Colored images of potatoes are obtained by industrial cameras, and an image processing module is used for conducting analysis processing on the obtained images. The method is characterized in that the convolutional neural network is adopted for detecting the surface defects of the potatoes,a training set serves as a sample for training the convolutional neural network, then a prediction set is input to the network after training, and a softmax classifier is adopted for achieving classification of the potatoes. According to the method, feature extraction is not required, there is no need to set specific detection algorithms according to defect types, the images are directly input tothe network, and therefore the potato surface defect detection method has the advantages of being high in accuracy, short in algorithm running time, high in feasibility and robustness and the like, and has a significant meaning in the potato processing industry.

Liu Fei

Papers

Potato surface defect detection method based on convolutional neural network