关键词: 深度学习, 手术器械自动化管理, 语义分割, 注意力机制

Abstract:

An improved Deeplab v3+ network model semantic segmentation algorithm with dynamic learning features is proposed for the problems of high-level labor cost and low-level intelligence in the management of domestic surgical instruments. In order to strengthen the effective feature learning of related tasks, the CBAM module of the attention mechanism is embedded into the Deeplab v3+ encoder and the high-level features of images are extracted through dense depth wise separable convolution and dilated convolution. Two low-level feature sources are added to the decoder, which retains important feature information and improves segmentation accuracy. The experimental results show that the MIoU, PA, Recall, and F-measure value of the improved network on the surgical instrument dataset are 0.854, 0.874, 0.872, and 0.873, respectively. Compared with other semantic segmentation networks, this improved network segmentation performance is better and has great engineering and practical value.

Key words: deep learning, automatic management of surgical instruments, semantic segmentation, attention mechanism