For transporting potatoes, it is proposed to equip the vehicle with elastic partitions. Elastic partitions are formed by tubes of elastic material and can rotate around the axis of the transverse partition. A model has been developed that allows one to take into account a large number of parameters of root crops, a vehicle body, elastic partitions, parameters of the unloading process, and also calculate the main indicators of the efficiency of unloading root crops. As a result of optimization, it has been found that the optimal length of the elastic tube is 1.58 m (when the height of the vehicle body is 1.60 m), the optimal coefficient of bending stiffness of the tube is 0.9 kN/m2. At the same time, the unloading time will be less than 11.5 s and the share of damaged root crops will be less than 4.0%.
Research purpose: development of a technology that will detect various types of vehicles in the image. To achieve this purpose it is necessary to solve the following objectives: Highlighting requirements for the technology being developed; Development of the technology for finding an object in an image; The choice of methods and algorithms for the allocation of objects for the development of the technology; The choice of methods and algorithms that allow detecting objects of a certain class; Implementation of the developed technology in the software system. The scientific novelty of the work lies in the application of previously known methods, which have been shown to be effective in other studies, to a new object of study, namely, to detect in images vehicles of various classes used for the transportation of agricultural goods. The practical value of the expected results lies in creating the software based on the developed technology, which makes it possible to detect vehicles of a certain class with a high degree of probability in a static image. The development of such software will automate part of the business processes and reduce labor costs. The following methods are used as the main ones in the developed technology: the HOG method (histograms of oriented gradients) and the support vector method (SVM).
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