Surgical instrument segmentation algorithm based on improved DeepLab-V3+

Endoscope-based surgical robots have become prevalent in clinical settings, offering precise segmentation information of surgical instruments to robotic systems. However, existing surgical instrument segmentation algorithms often face challenges in real-time processing and accuracy due to complex environmental factors. This paper introduces an improved DeepLab-V3+ segmentation algorithm to address these issues.The proposed method utilizes MobileNetV2 as the backbone network and introduces an improved space pyramid pooling module to enhance channel features. To validate the effectiveness of this method, experiments were conducted using a public endoscopic surgery dataset for both quantitative and qualitative analysis. Results from both types of experiments demonstrate the efficacy of the proposed method. The average mIOU reaches as high as 88.90%, indicating accurate and stable segmentation of surgical instruments.