TARGET DETECTION AND ANALYSIS OF INTELLIGENT AGRICULTURAL VEHICLE MOVEMENT OBSTACLE BASED ON PANORAMIC VISION

Agricultural automation and intelligence have a wide range of connotations, involving navigation, image, model, strategy and other engineering disciplines. With the development of modern agriculture, intelligent agricultural vehicles are applied in many engineering areas. The operating environment of agricultural vehicles is very complex, especially as they often face obstacles, affecting the intelligent operation of agricultural vehicles. The traditional obstacle detection mostly uses the limited detection algorithm, in the case of which it is difficult to achieve the moving target detection of panoramic vision. In this paper, mean shift algorithm is selected to detect the moving obstacles of intelligent agricultural vehicles, and adaptive colour fusion is introduced to optimize the algorithm to solve the problems of mean shift. In order to verify the effect of the improvement and application of the algorithm, the video image obtained by the intelligent agricultural vehicle is selected for the simulation experiment, and the best combination (- 0.8.0.2) is obtained for the unequal spacing sampling method. In the process of colour selection, the coefficient needs to be adjusted continuously to improve the tracking accuracy of the algorithm. Further it can be seen that when using a variety of different quantitative methods for comparative analysis, the quantitative method of HIS-360 level is determined.