关键词: 美国白蛾幼虫网幕, 阈值分割, 卷积神经网络, 视觉伺服

Abstract: In order to identify the location of Hyphantria cunea larvae nets more quickly and accurately, the spraying robot was controlled to spray the target, in this paper, a fast recognition algorithm based on chromatic difference threshold segmentation method and improved convolutional neural network (CNN) was proposed for the screen image of American white moth larvae. Firstly, the threshold segmentation based on color difference was carried out on the image, the probability of containing the screen was preliminarily determined, and the qualified image with the probability higher than a certain value was determined as the qualified image. The qualified image was sent to the convolution neural network, the image was non coincident traversed, the screen outline frame was marked, and further judge whether the optimal spraying position was reached. When the optimal spraying position was reached, the system stopped moving for spraying, when the image was unqualified or did not reach the best spraying position, the program controlled the actuator to continue to move according to the planned walking path. The experiment showed that the accuracy of screen recognition was more than 95%, the image recognition time was less than 200 ms, and there was no leakage in the experiment.

Key words: Hyphantria cunea larvae nets, threshold segmentation, convolutional neural network, visual servo