Dongyang Li

Measuring interpupilary distance and pupil height is a crucial step in the process of optometry. However, existing methods suffer from low accuracy, high cost, a lack of portability, and limited research on studying both parameters simultaneously. To overcome these challenges, we propose a method that combines ensemble regression trees (ERT) with the BlendMask algorithm to accurately measure both interpupillary distance and pupil height. First, we train an ERT-based face keypoint model to locate the pupils and calculate their center coordinates. Then, we develop an eyeglass dataset and train a BlendMask model to obtain the coordinates of the lowest point of the lenses. Finally, we calculate the numerical values of interpupillary distance and pupil height based on their respective definitions. The experimental results demonstrate that the proposed method can accurately measure interpupillary distance (IPD) and pupil height, and the calculated IPD and pupil height values are in good agreement with the measurements obtained by an auto-refractometer. By combining the advantages of the two models, our method overcomes the limitations of traditional methods with high measurement accuracy, low cost, and strong portability. Moreover, this method enables fast and automatic measurement, minimizing operation time, and reducing human errors. Therefore, it possesses broad prospects for application, particularly in the fields of eyeglass customization and vision inspection.

Wearing safety harness is essential for workers when carrying out work. When posture of the workers in the workshop is complex, using real-time detection program to detect workers wearing safety harness is challenging, with a high false alarm rate. In order to solve this problem, we use object detection network YOLOv5 and human body posture estimation network OpenPose for the detection of safety harnesses. We collected video streams of workers wearing safety harnesses to create a dataset, and trained the YOLOv5 model for safety harness detection. The OpenPose algorithm was used to estimate human body posture. Firstly, the images containing different postures of workers were processed to obtain 18 skeletal key points of the human torso. Then, we analyzed the key point information and designed the judgment criterion for different postures. Finally, the real-time detection program combined the results of object detection and human body posture estimation to judge the safety harness wearing situation within the current screen and output the final detection results. The experimental results prove that the accuracy rate of the YOLOv5 model in recognizing the safety harness reaches 89%, and the detection method of this study can ensure that the detection program accurately recognizes safety harnesses, and at the same time reduces the false alarm rate of the output results, which has high application value.