Сегментация перекрывающихся изображений деревьев на цифровых снимках лесных массивов

Abstract

The use of decision support systems based on computer vision and artificial intelligence significantly improves the working conditions for the operators of technological machines in the timber sector, whose work implies high intensity and psycho-emotional overload. By means of computer vision and artificial intelligence the operator can quickly and easily obtain the data on the state of the cutting area and adopt the optimal solution for holding the working operation. This facilitates his work and reduces the time spent searching and analyzing the data on the cutting area. Meanwhile, one of the key elements of such a system is a subsystem for automatic segmentation of objects in the photograph. We have explored the possibility of segmenting overlapping objects in the photographs of forest areas using a convolutional neural network based on the Mask R-CNN architecture. Unlike in most works on similar topics, the objects of this study are color photographs taken by an RGB camera rather than a lidar. This creates the prospect for reducing the cost of hardware and software systems used to support decision-making by the operators of logging machines. The images of the stems and crowns of coniferous and deciduous trees overlapping each other are the segmented objects under consideration. Using the GIMP graphic editor, we have manually marked the color photographs depicting a total of 134 trees of 4 different species: spruce, aspen, birch and pine. Utilizing the developed database, we have carried out an experiment to further train the Mask R-CNN convolutional neural network for segmentation of overlapping parts of the trees in the digital photographs of forest areas. The neural network has been pre-trained using the Microsoft COCO dataset containing more than 200,000 images of 80 different classes of objects such as people, cars, animals and various items. While training the neural network, the images supplied to its input were subjected to a series of various linear and nonlinear geometric transformations, which made it possible to increase the volume of training data by 11 times. As a result, the accuracy of segmentation of the images of the stems and crowns of coniferous and deciduous trees overlapping each other has reached 79 %, which allows the use of neural networks based on a similar architecture in decision support systems for logging machine operators.