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Using the YOLOv3 algorithm with preand post-processing procedures for fruit detection by an apple-picking robot

The development of robotic harvesting can help reduce the share of heavy manual labor in horticulture that reaches now 40%, as well as crop shortages, which reach up to 50%. Fruit picking robots have been developing since the late 1960s. However, no existing prototype is used in practice due to the low speed of harvesting and the large proportion of unrecognized fruits remaining on trees. The paper aims to develop an algorithm for detecting apples in images that can work quickly and find as many apples as possible. For this purpose, the use of the YOLOv3 convolutional neural network has been proposed, accompanied by special pre- and post-processing procedures. The procedures aim to improve the quality of apple recognition, including situations of the presence of shadows, glare, various damages to apples, empty gaps between the leaves, which could be mistaken for apples, overlapping apples by branches, leaves, and other apples. The algorithm recognizes both red and green apples. It can work with images of single apples in close-up photographs as well as with images of many apples in general pictures. The algorithm quality was evaluated on a test set of 818 images of red and green apples (5142 apples in total). The average apple detection time was 19 ms, the percentage of objects mistaken for apples turned out to be at the level of 7.8%, and the share of undetected apples at 9.2%. Both the average detection time and the error rates turned out to be noticeably shorter than in all known similar systems. Read more...

Applications of computer vision in the mining industry

In the last decade, there has been an active digitalization of industrial production based on rapidly developing information technologies, including artificial intelligence technologies. This is largely due to the development of deep learning methods and their applications in computer vision. Since the mid 2010s convolutional neural networks demonstrate exceptional efficiency in solving problems such as the detection, classification and segmentation of various objects. As a result, computer vision methods are beginning to be actively used in the problems of quality control of raw materials and finished products. All this applies to the mining industry. However, in the Russian scientific literature there are practically no systematic reviews of computer vision applications in this area. The present study aims to fill this gap. The paper provides a systematic review of the history of development and the current state of the methods and technologies of machine vision used in the mining industry for the analysis of solid materials, demonstrates the latest achievements in this area and examples of their application in the mining industry. The authors have analyzed 29 research papers in the field of application of computer vision in the mining industry and classified the stages of technology development from the mid-1980s, when computer vision was used without the use of machine learning, and ending with modern research based on the use of deep convolutional neural networks for solving problems of classification and segmentation. The effectiveness of the methods used is compared, their advantages and disadvantages are discussed, and forecasts are made for the development of computer vision methods in the mining industry in the near future. Examples are given showing that the use of convolutional neural networks made it possible to move to a qualitatively higher level of quality in solving problems of classification and segmentation as applied to the analysis of output volume, particle size distribution, including flakiness, angularity and roughness, dust and clay content, bulk density and emptiness, etc. Read more...

Using deep learning for rock segmentation in mining conveyors and warehouses

In the last decade, the introduction of artificial intelligence methods in industry has been accelerating. The development of deep learning algorithms and the emergence of the ability to store and process large amounts of information make it possible to quickly and efficiently automate tasks that previously could only be solved by people – employees of enterprises, and the results obtained not only correspond to human cognitive abilities, but often surpass them. An interesting example of a routine task that can be automated using computer vision methods is the task of segmenting stones on conveyors and warehouses of mining enterprises to ensure quality control of raw materials and finished products. The purpose of this work is to develop an algorithm for segmenting stones on conveyors and warehouses. To achieve this goal, a brief historical review of approaches to solving the described problem was carried out, and a study was made of the application of the Mask R-­CNN architecture to solving the problem of stone segmentation. The training dataset included 1000 augmented images from 100 crushed stone photos taken on a mining conveyor belt. The results obtained in the IoU metric exceeded 83 %, and in the Accuracy metric – 89 %, which provides high-quality automatic continuous visual quality control of raw materials or finished products. The resulting segmentation maps can serve as a good basis for determining granulometric characteristics, quality categories that are important in the mining industry, timely detecting flakiness on conveyors and segregation in finished product warehouses in real time. Read more...