Intelligent unmanned vending machine system in machine vision

The intelligent unmanned vending machine system in machine vision and the static identification of Guangdong vending machine is that the server obtains the data of the pressure sensor from each grid. Through data analysis, the quality of the grid is changed to obtain which grid the customer took out. The unmanned vending machine At the same time, the front-end camera takes the image of this grid and uploads it to the server after the front-end processing. On the server, the image is detected, located and recognized through the deep learning algorithm (YOLO). The YOLO model based on convolutional neural network was proposed in 2015 to be able to detect and recognize objects in real time. It is one of the network models with the best combination of position detection accuracy and recognition accuracy for objects. It is also the network with the best real-time performance. The model model uses a convolutional neural network structure. The convolutional layer of the model extracts image features and the fully connected layer predicts the output probability. The model structure is similar to the network model and the final output is the network model shown in Figure 3. The system is optimized on the basis of this network model to modify the fully connected layer and the convolutional layer. The hardware of the system adopts embedded quad-core ARM9 as the front-end image acquisition controller and uses it to realize the entire intelligent unmanned retail system. The data information on each unit module is summarized, analyzed and processed, and control instructions are issued to each functional module to coordinate the stable operation of the entire system. The structure of each unmanned vending container is divided into 4 layers and 2 columns, a total of 8 grids, and each grid contains one type of commodity. A 5 million pixel CMOS camera is installed above each grid to statically collect the quantity of goods in each grid. A pressure sensor is installed under each grid to determine which type of commodity the customer has taken, and then the image of this grid is sent to the server for identification and counting to accurately determine how many commodities the customer has taken. At the same time, the data of the pressure sensor is sent to the server database for analysis and comparison. A 10-megapixel camera is installed at the top of the cabinet to dynamically collect the types of commodities. At the same time, two pairs of infrared sensors are installed in the front of each grid of the cabinet. When the infrared sensor detects that the customer has finished taking the product, the camera on the top of the cabinet dynamically shoots the product in the customer's hand. The central controller compresses the collected image at the front end and uploads it via WiFi or 4G module. To the server. The software and hardware design of the intelligent unmanned retail vision system; realizes the static and dynamic shooting of images by the front-end hardware camera module group, the data transmission of the pressure sensor group, the data collection of the infrared sensor group and the communication between the various modules; software Completed the detection, positioning and recognition of the image by the neural network; through the combination with the front-end APP and the back-end database, a new intelligent unmanned new retail system can be realized. The application of the artificial intelligence vision system to the new retail industry enables customers to scan the door and pick up the goods themselves. , The new experience of closing the door and automatic settlement facilitates customers and saves product costs. Hope that the above content introduced by the Guangdong unmanned vending machine is helpful to everyone. If you want to know more information, please continue to wait for the author to share it. Previous: The principle of unmanned vending machine information is perceivable Next: The feasibility of vending machine shared design