关键词: 黄花菜, 采摘机器人, 神经网络, 机器视觉, 图像处理, 目标检测

Abstract: The traditional target detection algorithm can only get the target frame, and cannot determine the growth direction of daylily. Aiming at this problem, the neural network structure is optimized on the basis of the existing target detection algorithm, and the prediction of the detection box is changed to the prediction of the key points. Firstly, the growth direction and length of daylily are determined according to the anchor point matching method, and the growth angle and length of the daylily target are counted. Multiple anchor points are set based on the statistical results. The actual growth angle and length are compared with the anchor points to obtain the relative length and angle of the target, which is used as the model prediction value for training. Secondly, the heat map prediction branch is added to the model to predict the four key points. Finally, the growth posture of daylily target is obtained by using the target length and angle information to connect the key points. An evaluation model method for line segment fitting characteristics is designed, Introduction of Partial Affinity Fields in Calculation Accuracy, and the NonMaximum Suppression algorithm is improved accordingly. Through experimental verification, the accuracy of picking target recognition is 91.02%, the positioning accuracy is 99.8%.

Key words: daylily, picking robot, neural network, machine vision, image processing, object detection