A Novel Robot System for Hair Transplant Surgery Based on Self-Calibration and Structured Light Hair Follicle Detection

Recently, hair transplantation is becoming more and more popular, however, traditional hair transplantation procedures follicular unit extraction(FUE) and follicular unit transplantation(FUT) can only achieve the migration of hair follicles, and follicular unit multiplication(FUM) technology, which can achieve the multiplication of hair follicles, has not been able to come into people's view due to the limitation of the lack of hair follicle positioning accuracy and the difficulty of controlling the angle of hair follicle cutting. In order to improve the accuracy of hair follicle detection, we design a hair transplant robot system based on SVM calibration, and propose a three-stage structured light hair follicle detection scheme, among which we propose an improved algorithm DCSB- Yolox based on Yolox in the detection part, where the proposed DCSB module is able to fuse the channel and spatial information of the backbone and Neck parts of the network, and the precision and recall of our hair follicle detection method reach 0.956 and 0.960 respectively. Finally, we conducted a real-world test using a dummy head and found that the average error of the Euclidean distance accuracy of the system is 0.256mm, which meets the requirements of FUM hair transplantation surgery.