The invention discloses a panoramic-vision potato sorting and defect detection device which comprises a conveying device, a detection camera obscura, a sorting mechanism, an infrared sensor module, an image acquisition mechanism, an image processing-analyzing module, a data fusion module and a time sequence module, wherein a convolutional neural network and a support vector machine I (SVM I) are built in the image processing-analyzing module; an SVM II is built in the data fusion module; and the time sequence module is used for coordinating actions of all parts. The invention further discloses a potato sorting detection method using the above device. According to the device and the potato sorting detection method disclosed by the invention, all-around detection can be completed without turning over potatoes in a detection process. Accordingly, the potatoes can be protected from unnecessary damage on the one hand, and on the other hand, the instability during dynamic photo taking and detection can be avoided, and the image clarity and the detection accuracy can be improved. The device and the potato sorting detection method can be widely applied to real-time online detection of the external quality of potatoes, namely a kind of agricultural product, and has significance for promoting development of the Chinese potato industry.

Panoramic-vision potato sorting and defect detection device as well as sorting detection method thereof

The invention provides a pulmonary nodule hierarchical judgement method and system based on deep learning and data fusion; the method comprises: pretreating a plurality of CT (computed tomography) pulmonary images to obtain pretreated CT pulmonary nodule images; extracting image characteristics of the CT pulmonary nodule images, and pre-classifying the CT pulmonary nodule images according to the image characteristics; subjecting the image characteristics of the pre-classified CT pulmonary nodule images and corresponding comprehensive clinical consulting data to repeated training and iteration to obtain a diagnostic model; performing CT imaged pulmonary nodule hierarchical judgment on the CT pulmonary images based on the diagnostic model By using a convolutional neural network model in deep learning in combination with the multi-source data integration technology, the method special for hierarchically judging CT pulmonary nodules is acquired via verification and testing; the judging process is comprehensive and reliable, accuracy of judging results is effectively improved, and doctors can be effectively assisted in diagnosing diseases

Pulmonary nodule hierarchical judgement method and system based on deep learning and data fusion

The invention discloses a multi-stage combined intelligent defect detection system for potatoes. The system comprises a dynamic weighing pre-selecting mechanism, detecting wires and an electric control device; three detecting wires are arranged; each layer of detecting wire comprises an upper-layer slideway, a photosensitive array shape detecting mechanism, a lower-layer slideway and a whole surface defect detecting mechanism; and the upper-layer slideways and the lower-layer slideways are both arranged in a downward inclination manner. The invention further discloses a detection method using the multi-stage combined intelligent defect detection system for the potatoes. According to the multi-stage combined intelligent defect detection system for the potatoes, graded detection of the weight, the shape and the surface defect of the potatoes is achieved according to the shape, the weight and the appearance characteristic of the potatoes by means of multi-stage combined intelligent detection, the micro focus large-breadth short distance panoramic visual inspection can be performed on the upper surfaces and the lower surfaces of the potatoes without photographing under the situation that the potatoes are not overturned, and the accuracy of detection is improved.

Multi-stage combined intelligent defect detection system for potatoes and detection method

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 panoramic potato defect detection device and method based on combination of virtual and real imaging. The detection device comprises a conveying device, a darkroom detectiondevice, a control module and an intelligent algorithm defect identification module; the control module is connected with the conveying device, the darkroom detection device and the intelligent algorithm defect identification module; and the control module controls starting and stopping of the conveying device and the darkroom detection device, and controls the conveying device according to a result of the intelligent algorithm defect identification module, so that classification is completed. The darkroom detection device comprises a camera, an annular LED lamp, a baffle push rod mechanism, anupper box body, a lower box body, a transparent plate, a gear rack mechanism, a spring, a supporting rod, a support sleeve, a reflector mechanism and a bottom LED lamp. According to the detection device and method, the single camera is used; a real image of the upper part of a potato and virtual images of four reflectors can be formed at the same time; and panoramic defect detection is completedunder the condition of not turning over the potato.

Panoramic potato defect detection device and method based on combination of virtual and real imaging

The utility model discloses a panoramic vision potato is selected separately and defect detecting device, including conveyor, detect camera bellows, sorting mechanism, infrared sensor module, image acquisition mechanism, built -inly have convolutional neural network and SVMs SVM's image processing analysis module, a built -in timing module who has SVMs SVM's data fusion module and be used for coordinating each part action The utility model discloses a panoramic vision potato is selected separately and defect detecting device need not the potato and overturns the motion can accomplish the omnidirectional and detect in the testing process, has avoided the unneccessary damage of potato on the one hand, and on the other hand has avoided the developments instability in detecting that shoots, improves image definition, has promoted the rate of accuracy that detects The utility model discloses can extensively be used for the real -time on -line measuring of potato agricultural product external sort, it is significant to the development that promotes china's potato industry

Liu Chaojie

Papers

Panoramic-vision potato sorting and defect detection device as well as sorting detection method thereof

Panoramic vision potato is selected separately and defect detecting device