基于Mask R-CNN结合边缘分割的颗粒物图像检测

对颗粒物的尺寸检测是生产中重要的环节,使用相机采集图像并处理是常用的非接触检测方法。围绕颗粒物的识别与尺寸检测需求,选用沙粒为检测对象,提出了一种改进颗粒物边界掩膜的Mask R-CNN模型。该模型结合经典的边缘检测技术,并利用深度学习模型预测掩膜,根据边缘分割的结果来得到更高精度的掩膜。使用DenseNet作为检测网络的主干网络,使得整体网络参数量更少,并利用通道注意力机制加强网络的特征提取能力。实验结果表明,改进的网络可以提高检测的精度,且结合图像处理的方式能够改善掩膜尺寸检测的准确度,为颗粒物的工业检测提供了一种有意义的方法。

Particles image detection based on Mask R-CNN combined with edge segmentation

Particles size detection is an important link in production, and the use of cameras to capture and process images is a commonly-used non-contact detection method. To meet the requirements of identification and size detection of particles, the sand particles were selected as the detection object, and a Mask R-CNN model with the improved boundary mask of particles was proposed. Combined with the classical edge detection technology, the deep learning model was used to predict the mask, and the mask with higher precision was obtained according to the results of edge segmentation. The DenseNet was used as the backbone network of the network detection to reduce the number of network parameters, and the channel attention mechanism was used to strengthen the feature extraction ability of the network. The experiments show that the improved network can improve the detection accuracy, and the combination of image processing can improve the accuracy of mask size detection, which provides a meaningful method for industrial detection of particles.