Research And Implementation Of Object Grasping Recognition Algorithm Based On Computer Vision

Computer vision technology is one of the main research directions of artificial intelligence.With the rapid growth of image or video data size and the improvement of computing power,computer vision technology has achieved unprecedented development in recent years and is widely used in various scenarios,e.g.driverless and crowd monitoring.This paper implemented a set of intelligent recognition algorithms and successfully applied it to automatic checkout retail.This paper designs an object capture recognition algorithm based on deep learning and computer vision technology.It can identify the number and category of target objects in the state where the target is hold by hand.It can be used to identify items held by customers in a retail scene.It contains two algorithm modules,which are stereo matching algorithm and object recognition algorithm.The main contents are as follows:In order to expose the target object to multiple angles of view in the field of view to obtain a multi-angle information source,and avoid the single camera being occluded and the algorithm completely failed.In this paper,a multi-camera erection scheme is adopted to design and implement a fast stereo matching algorithm.The algorithm utilizes the polar constraints of the binocular camera and dynamic programming,completing the matching in linear time and locating the same object between multiple cameras.The object recognition algorithm is the core of this paper.The retail scene needs to identify the type of target and the corresponding number.Although the target detection algorithm can achieve this purpose,it requires a lot of labeling cost and calculation cost.This paper utilized weak supervised learning,designed a deep convolutional neural network and a counting loss function.The number and type of target objects in the image can be identified without labeling the position information of the target object.Moreover,this paper have designed an evaluation function for this visual recognition task,which is stricter than the traditional accuracy and recall rate.Under our data set,this paper achieved 94.6% accuracy,and the forward time of the model is only 3.87 milliseconds.Action Recognition uses Two-Stream Neural Network to extract temporal feature and spatial feature,then identify the purchase behavior made by target customer in the store.Finally,this paper also designed an automatic labeling algorithm for video data,which uses interpolation algorithm and convolutional neural network to accurately predict the target Bounding Box,which greatly reduces the cost of manual labeling.