Vision Based Real-time High-accuracy Automatic Counting with Applications for Smart Pharmacy

At present, many industrial applications urgently need a real-time high-accuracy automatic counting algorithm to realize the counting function of various objects. Some scholars are studying the use of deep learning algorithms to count objects in industrial applications. However, facing closely arranged and different objects, deep learning algorithms cannot achieve this function. Aiming at the application background of regular cuboids counting with a wide range of requirements, this paper provides a real-time high-accuracy automatic counting algorithm based on a deep understanding of the scene and a comprehensive use of multiple computer vision technologies. The algorithm can meet the stringent requirements of industrial applications and realize real-time high-accuracy automatic counting of closely arranged cuboids. And there is no requirement for whether the surfaces of the cuboids are the same, and there is no need to prepare the template pictures of the cuboids in advance. The algorithm mainly uses the characteristics of gaps between cuboids, and adopts improved Hough line detection to identify gaps, and finally realizes the counting function. The algorithm proposed in this paper has been verified on the counting of pillboxes in the industrial application of smart pharmacies. Compared with the deep learning algorithm, it has the characteristics of good interpretability, good generalization, fast speed and high accuracy.