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.

中文翻译：

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基于图像处理和改进算法的篮球目标识别研究

本文研究了基于图像处理和改进算法的篮球进球识别方法，以提高篮球进球自动识别的准确性。红外光谱图像采集系统用于采集篮球球门图像。采用自适应滤波算法对图像进行去噪后，利用小波分析方法提取篮球进球信号的特征，输入到优化后的可变形卷积神经网络中。通过每个采样点的值与块卷积核对应的位置权限加权求和，将结果作为卷积运算输出。结合相同维度的深度特征，得到候选目标区域的全连接特征，实现篮球目标识别。实验结果表明：该方法能有效识别篮球目标，识别错误率低；篮球进球自动识别结果平均准确率高达98.4%；在不同程度的噪声影响下，该方法受噪声影响较小，抗干扰能力强。