Method for generating target detection football candidate point of Nao robot based on Heatmap
A target detection and candidate point technology, applied in neural learning methods, instruments, computer parts, etc., can solve real-time limitations, low accuracy, low robustness and other problems, achieve high real-time, accurate classification, high The effect of recognition accuracy
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[0038] The present invention will be further described below in conjunction with the drawings and implementation cases:
[0039] Such as figure 1 , 2 Shown is a Heatmap-based Nao robot target detection football candidate point generation method, including: S1: clear design goals and the hardware level of the experimental carrier Nao robot, based on the deep learning method, determine the convolutional neural network as the target detection model , Refer to YoloV3 detection algorithm (target detection algorithm), select the deep learning training framework DarkNet. Build a target detection model with six convolutional layers and one output layer for training. The network structure is as follows image 3 Shown.
[0040] S2: Simulate the competition environment, obtain physical photos from the camera of the Nao robot, collect a large number of pictures, and use LabelImg software to label and organize the results. Figure 4 The VOC data set (target detection data set) shown is used for...
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