Mid-infrared imaging systems are widely applied in gas-leak detection. However, infrared images generally suffer from low contrast and poor quality. In this paper, an image-enhancement method based on Gaussian filtering and adaptive histogram segmentation is proposed to effectively improve the quality of infrared images. It can effectively improve the quality of infrared images, which contributes to the subsequent gas-image feature extraction. The traditional background modeling algorithm is analyzed, and the ViBe (visual background extractor) algorithm is studied in depth. Based on the advantages and disadvantages of the ViBe algorithm and the characteristics of gas-leak images, a gas-leak region detection method based on the improved ViBe algorithm is proposed. The test results show that it can quickly establish a background model, segment the gas-leak region with motion characteristics, and render the gas-leak region in color based on grayscale mapping to achieve the automatic detection and enhanced display of gas leaks.

This paper develops a high-precision miniature three-dimensional Fiber Bragg Grating (FBG) force sensor designed for detecting and locating hard masses within tissues during Minimally Invasive Surgery (MIS) and robotic-assisted MIS palpation processes. The prototype consists of three FBG-etched optical fibers and an integrated elastomer. The fibers are suspended and parallel to each other within the elastomer, allowing each FBG to be directly compressed or stretched under stress. A novel model, combining segment-by-segment sensitivity and differential decoupling, is adopted for decoupling three-dimensional forces. This approach significantly reduces the crosstalk error caused by spatial forces, avoids the complexity of assembling multiple fibers in previous designs, and reduces the sensor's size, facilitating its integration into MIS procedures. Calibration experiments are conducted to determine the sensor's force sensitivity coefficients, achieving resolutions of 4.35 mN, 3.99 mN, and 3.30 mN in the X, Y, and Z directions, respectively. Through discrete and drag palpation tests on silicone with different types of hard inclusions, the results demonstrate that the proposed prototype has the ability to effectively identify the size, hardness, and depth of hard objects from the contact force information. Additionally, ex vivo porcine kidney palpation experiments are conducted to further verify the feasibility of locating hard masses during MIS palpation.

Objective: To accurately achieve distal contact force, a novel temperature-compensated sensor is developed and integrated into an atrial fibrillation (AF) ablation catheter. Methods: A dual elastomer-based dual FBGs structure is used to differentiate the strain on the two FBGs to achieve temperature compensation, and the design is optimized and validated by finite element simulation. Results: The designed sensor has a sensitivity of 90.5 pm/N, resolution of 0.01 N, and root–mean–square error (RMSE) of 0.02 N and 0.04 N for dynamic force loading and temperature compensation, respectively, and can stably measure distal contact forces with temperature disturbances. Conclusion: Due to the advantages, i.e., simple structure, easy assembly, low cost, and good robustness, the proposed sensor is suitable for industrial mass production.

Laser range-gated underwater imaging technology, by removing most of the backscattering noise, can effectively increase image contrast and extend the detection range. The optical signal captured by a range-gated imaging system primarily comprises reflected light from the object and backscattered light from the surrounding water. Consequently, surfaces with low reflectivity or highly turbid water environments substantially constrain the applicability of the range-gated imaging system. To enhance the detection capability of underwater laser range-gated imaging, this paper proposes the incorporation of underwater polarized light imaging technology as an enhancement method. Based on polarization differences, backscattered light and reflected light from an object can be distinguished. Experimental results indicate that, compared to images obtained using a conventional range-gated laser imaging system, those captured with a polarization-enhanced system exhibit an increase of up to 47% for the corresponding Enhancement Measure Evaluation (EME) index. The proposed approach, which integrates polarization imaging with range-gated laser imaging, has the potential to broaden the applicability of underwater laser imaging scenarios, such as deep-sea exploration and military applications.

When grabbing an object, a robot needs to be installed with a sensor on its finger to sense the position of the object and the intensity of the contact force. Therefore, the accuracy of tactile sensor’s recognition is very important to grasp the object successfully. In our work, we design a data acquisition scheme and establish the corresponding deep learning dataset taking the optical-fiber-based tactile sensor as the research object. This kind of sensor is composed of different layers. Those layers are deformed when the force is applied on the surface. Since the extent of deformation is proportional to the value of the force, the color and brightness of the collected pictures will change according to Poisson effect. A novel multi-scale ResNet is proposed and compared with some mainstream networks such as AlexNet, ResNet, VGG, DenseNet, and GoogLeNet under the same dataset. The dataset we collected contains normal force, shear force, and torsion. It can provide better calibrations between the image change and force value. The proposed network is investigated experimentally. The results demonstrate that it can achieve enhanced performance by extracting image features of different scales. This multi-scale ResNet can be widely applied to the deep learning training of mechanics sensors based on the image and is also well-suited for the calibration tasks such as classification and regression prediction of mechanics sensors based on multiple data features.