| Summary: | This paper studies the basketball goal recognition method based on image processing and improved algorithm to improve the accuracy of automatic recognition of basketball goal. The infrared spectrum image acquisition system is used to collect the basketball goal image. After the image is denoised by using the adaptive filtering algorithm, the wavelet analysis method is used to extract the features of basketball goal signal, which are input into the optimized deformable convolution neural network. Through the weighted sum of the values of each sampling point and the corresponding position authority of the block convolution core, the results are output as convolution operation. Combined with the depth feature of the same dimension, the full connection feature of the candidate target area is obtained to realize the basketball goal recognition. The experimental results show the following: the method can effectively identify basketball goals and the recognition error rate is low; the average accuracy of the automatic recognition results of basketball goals is as high as 98.4%; under the influence of different degrees of noise, the method is less affected by noise and has strong anti-interference ability.
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