肺炎是一种严重威胁人类健康的疾病,及时、准确地检测出肺炎可以尽早帮助患者接受治疗。因此,提出了一种基于YOLOv3改进的Multi branch YOLO检测算法。用多分枝膨胀卷积输出的特征代替YOLOv3中不同层级的特征进行检测,在多分枝卷积神经网络中引入Boosting思想,并使用最大化熵方法优化网络。将每个卷积分枝视为一个弱分类器,通过最大化熵方法使每个分枝学习到相近的检测能力,避免多分枝卷积模型退化成单分枝卷积模型。基于北美放射学会提供的肺部X射线影像进行实验,结果表明,该算法在实验数据集上的检测准确率高于其他目标检测算法。

Pneumonia is a disease that serious threat to human health, timely and accurate detection of pneumonia can help patients receive treatment as soon as possible. Therefore, in this paper, an improved Multi branch YOLO detection algorithm based on YOLOv3 is proposed. The output features of multi branch dilation convolution are used to replace the features of different levels in YOLOv3 for detection. Boosting thought is introduced into multi branch convolutional neural network, and the network is optimized with maximum entropy approach. Each convolution branch is regarded as a weak classifier, and the maximum entropy approach is adopted to promote each branch to learn the similar detection ability, so as to avoid the degeneration of multi branch convolution model into single-branch convolution model. Experimental data are provided by the radiological society of North America with lung X-ray images. The results show that algorithm's detection accuracy on experimental data sets is higher than other target detection algorithms.