Method for generating target detection football candidate point of Nao robot based on Heatmap

The invention relates to a method for generating a target detection football candidate point of a Nao robot based on a Heatmap. The method comprises the steps of selecting a convolutional neural network as a target detection model; simulating competition environments, collecting a plurality of groups of pictures to make a data set for training and testing, generating a Heatmap, processing to obtain a Heatmap visualization result, reconstructing the convolutional neural network to accelerate the network calculation speed, setting a proper threshold value, taking points greater than the set threshold value in the Heatmap as candidate points of a ball, and finally sending the candidate points into a classifier to obtain a final accurate identification result. According to the method, the adaptive capacity of the Nao robot vision system to the light environment of the competition field is enhanced; high-precision recognition of the football can be achieved in different light environments;feature extraction is completed through few convolution layers; the recognition real-time performance is guaranteed; and meanwhile the football recognition accuracy is greatly improved through the method that football candidate points are generated and then enter the classifier to be recognized.