Contagious infection-free medical interaction system with machine vision controlled by remote hand gesture during an operation.

Background and objective: Medical image visualization is a requirement in many types of surgery such as orthopaedic, spinal, thoracic procedures or tumour resection to eliminate risk such as "wrong level surgery". However, direct contact with physical devices such as mice or touch screens to control images is a challenge because of the potential risk of infection. To prevent the spread of infection in sterile environments, a contagious infection-free medical interaction system has been developed for manipulating medical images. Methods: We proposed an integrated system with three key modules: hand landmark detection, hand pointing, and hand gesture recognition. A proposed depth enhancement algorithm is combined with a deep learning hand landmark detector to generate hand landmarks. Based on the designed system, a proposed hand-pointing system combined with projection and ray-pointing techniques allows for reducing fatigue during manipulation. A proposed landmark geometry constraint algorithm and deep learning method were applied to detect six gestures including click, open, close, zoom, drag, and rotation. Additionally, a control menu was developed to effectively activate common functions. Results: The proposed hand-pointing system allowed for a large control range of up to 1200 mm in both vertical and horizontal direction. The proposed hand gesture recognition method showed high accuracy of over 97% and real-time response. Conclusion: This paper described the contagious infection-free medical interaction system that enables precise and effective manipulation of medical images within the large control range, while minimizing hand fatigue.

Revolutionizing Robotic Depalletizing: AI-Enhanced Parcel Detecting with Adaptive 3D Machine Vision and RGB-D Imaging for Automated Unloading.

Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parcels regardless of their shape, pattern, or layout. The core of the algorithm is the utilisation of RGB-D technology for detecting the primary boundary lines regardless of obstacles such as adhesive labels, tapes, or parcel surface patterns. For cases where detecting the boundary lines is difficult owing to narrow gaps between parcels, we propose using deep learning-based boundary line detection through the You Only Look at Coefficients (YOLACT) model. Using image segmentation techniques, the algorithm efficiently predicts boundary lines, enabling the accurate detection of irregularly sized parcels with complex surface patterns. Furthermore, even for rotated parcels, we can extract their edges through complex mathematical operations using the depth values of the specified position, enabling the detection of the wider surfaces of the rotated parcels. Finally, we validate the accuracy and real-time performance of our proposed method through various case studies, achieving mAP (50) values of 93.8% and 90.8% for randomly sized and rotationally covered boxes with diverse colours and patterns, respectively.

Numerical Simulation of the Effects of Scanning Strategies on the Aluminum Evaporation of Titanium Alloy in the Electron Beam Cold Hearth Melting Process.

In the production of titanium alloy, the electron beam cold hearth melting (EBCHM) process is commonly used due to its effectiveness and the high quality of the end product. However, its main drawback is the significant loss of elements such as aluminum (Al) due to evaporation under the vacuum environment. Numerical coupled thermal-flow models were here developed to investigate the effects of scanning strategies on Al loss in a titanium alloy during EBCHM. The validation model was successful in comparison with previously published experimental data. The Al mass fraction results at the outlet of the water-cooled hearth were strongly influenced by changes in the applied scanning strategies. The results indicated that the Al mass fraction loss could be reduced by using the full-hearth scanning strategies.