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Wood is one of the energy sources for sustainable development, which is closely related to our life. In the wood production process, it will be sorted according to different diameter grade is an essential link, at present the wood sorting work mainly rely on manual completion, not only low efficiency, high cost, and cannot meet the automated era of production needs. In this paper, a wood diameter level automatic sorting system is designed, first of all, using computer vision technology to measure the diameter of the wood section in real time, then the diameter data is transmitted to the PLC control system, and finally the PLC control system pushes out the wood in the specified position, realizing the automated on-line sorting of wood. In addition, a wood diameter measurement method based on deep learning combined with binocular vision is proposed, which can quickly and accurately recognize the diameter of each piece of wood, and provides a solid foundation for the efficient performance of the automatic wood diameter grade sorting system. The experimental results show that the measurement method proposed in this paper has high robustness and the maximum error is within 5mm, which meets the accuracy requirements needed for sorting, and the sorting system can sort 10 pieces of wood per minute on average, which greatly improves the efficiency of wood sorting and reduces the labor workload and cost, and the research design is of great significance to the wood production and manufacturing industry.

As brown mushrooms are both delicious and beneficial to health, the global production and consumption of brown mushrooms have increased significantly in recent years. Currently, to ensure the quality of brown mushrooms, selective manual picking is required, and the delicate surface of the mushrooms must not be damaged during the picking process. The labor cost of picking accounts for 50–80% of the total labor cost in the entire production process, and the high-humidity, low-temperature plant environment poses a risk of rheumatism for the laborers. In this paper, we propose a novel underactuated gripper based on a lead screw and linear bearings, capable of operating with flexible force control while simultaneously measuring the diameter of the mushrooms. The gripper features three degrees of freedom: lifting, grasping, and rotation, and enabling it to approach, grasp, and detach the mushroom. A thin-film force sensor is installed on the inner side of the fingers to achieve accurate grip force measurement. The use of a PID algorithm ensures precise grip force control, thereby protecting the brown mushrooms from damage. Experimental results demonstrate that the proposed gripper has a static grasping force error of 0.195 N and an average detachment force overshoot of 1.31 N during the entire picking process. The in situ measurement of the mushroom diameter achieves 97.3% accuracy, with a success rate of 98.3%. These results indicate that the gripper achieves a high success rate in harvesting, a low damage rate, and accurate diameter measurement.

We present a stereo image-based algorithm for tree stem diameter measurement and form analysis. The algorithm uses planar parametric curves to represent two-dimensional projections of tree stems in stereo images. The curves evolve according to an energy formulation based on the gradients of the images and inductive priors related to biomechanics and morphology of tree stems. After energy minimization, the curves are reconstructed to three dimensions, allowing for diameter measurements at any point along the height of the stem. We describe the algorithm and report the validation test results comparing predicted diameter measurements to external observations. Our findings demonstrate that the algorithm can automatically estimate diameters for trees within 20 m of the camera with an error of 5.52%. We highlight how this method can aid product value optimization through taper analysis and sweep or crook detection. A run-time analysis shows that the algorithm can estimate dendrometric variables for ten trees simultaneously at 15 frames per second on a consumer-grade computer. Furthermore, we discuss the opportunity to produce training data for machine learning algorithms that generalize across domains and eliminate the need to manually tune parameters.

This study aims to improve the Agaricus bisporus detection efficiency and performance of harvesting robots in the complex environment of the mushroom growing house. Based on deep learning networks, an improved YOLOv5s algorithm was proposed for accurate A. bisporus detection. First, A. bisporus images collected in situ from the mushroom growing house were preprocessed and augmented to construct a dataset containing 810 images, which were divided into the training and test sets in the ratio of 8:2. Then, by introducing the Convolutional Block Attention Module (CBAM) into the backbone network of YOLOv5s and adopting the Mosaic image augmentation technique in training, the detection accuracy and robustness of the algorithm were improved. The experimental results showed that the improved algorithm had a recognition accuracy of 98%, a single-image processing time of 18 ms, an A. bisporus center point locating error of 0.40%, and a diameter measuring error of 1.08%. Compared with YOLOv5s and YOLOv7, the YOLOv5s-CBAM has better performance in recognition accuracy, center positioning, and diameter measurement. Therefore, the proposed algorithm is capable of accurate A. bisporus detection in the complex environment of the mushroom growing house.

Cardiovascular disease is a severe threat to human health. The assessment of the degree of cardiovascular stenosis is very important for the diagnosis of cardiovascular disease. The segmentation of coronary angiographic images is the basis of analyzing the degree of stenosis. In this paper, a method that evaluates explicitly the degree of stenosis of blood vessels of interest is proposed. This method first extracts the vessels of interest by interactive segmentation, then thins it, and then calculates the diameter by edge intersection method. The average Dice Similarity Coefficient (DSC) value of the proposed segmentation method exceeds 92%, and the average Jaccard Similarity (JAC) value is over 86%. The area under the ROC curve value of the method is larger than the u-net method and the multiscale Hessian method. The results indicate that the interactive method can have good segmentation results and meet the general segmentation requirements. The qualitative and quantitative evaluation of the diameter measurement effect also shows that the effect of the corresponding diameter measurement method can reflect the change of the thickness of the blood vessel and basically meet the needs of clinical use.

In nuclear power plants, due to the special working environment, the status of the fuel rods will be changed by the influence of radiation. And these changes, such as diameter expansion, will cause damage to the fuel rods and even affect the safe operation of nuclear power plants. Therefore, it is necessary to accurate diameter measurement for nuclear fuel rods. This paper aims to solve the measurement problem of fuel rods, and proposes a measurement technique based on image processing. In the proposed scheme, based on Zhang's method, tangential distortion is considered to be an affecting factor at first to improve the accuracy of camera calibration. Then, a particle swarm algorithm with all-parameter adaptive mutation method is proposed for the optimization of camera parameters. Finally, a simple and convenient method to calculate pixel equivalent is designed by considering the feature extraction of corner coordinates of subpixel to accurately obtain pixel equivalents. The effectiveness of this measurement scheme is highlighted on a measuring platform. The relative error of the proposed method reaches 0.041%, and RMSE reaches 4.16 μm. Experimental results demonstrate that this approach is efficient for high-accuracy diameter measurement of fuel rods.

An optical strain gauge based on a balloon-like interferometer structure formed by a bent standard single-mode fiber combined with a 3D printer piece has been presented and demonstrated, which can be used to measure displacement. The interferometer has a simple and compact size, easy fabrication, low cost, and is repeatable. The sensor is based on the interference between the core and cladding modes. This is caused by the fiber’s curvature because when light propagates through the curved balloon-shaped interferometer region, a portion of it will be released from the core limitation and coupled to the cladding. The balloon has an axial displacement as a result of how the artwork was constructed. The sensor head is sandwiched between two cantilevers such that when there is a displacement, the dimension associated with the micro bend is altered. The sensor response as a function of displacement can be determined using wavelength shift or intensity change interrogation techniques. Therefore, this optical strain gauge is a good option for applications where structure displacement needs to be examined. The sensor presents a sensitivity of 55.014 nm for displacement measurements ranging from 0 to 10 mm and a strain sensitivity of 500.13 pm/μϵ.

Here, it is aimed to measure and detect the iron mass of a concrete solid wall, column, floor by using of ultra-wide band radar. Electromagnetic waves boundary conditions, reflection, and refraction of waves that radiate in solid material are analysed. GPR imaging methodology and scanning types such as A, B, and C are analysed. Ultra-wide band radar imaging is used for detecting measuring the diameter of the iron material.

Riveted workpieces are widely used in manufacturing; however, current inspection sensors are mainly limited in nondestructive testing and obtaining the high-accuracy dimension automatically is difficult. We developed a 3-D sensor for rivet inspection using fringe projection profilometry (FPP) with texture constraint. We used multi-intensity high dynamic range (HDR) FPP method to address the varying reflectance of the metal surface then utilized an additional constraint calculated from the fused HDR texture to compensate for the artifacts caused by phase mixture around the stepwise edge. By combining the 2-D contours and 3-D FPP data, rivets can be easily segmented, and the edge points can be further refined for diameter measurement. We tested the performance on a sample of riveted aluminum frame and evaluated the accuracy using standard objects. Experiments show that denser 3-D data of a riveted metal workpiece can be acquired with high accuracy. Compared with the traditional FPP method, the diameter measurement accuracy can be improved by 50%.

ABSTRACTThe geometric characteristics of synthetic macrofibers are important because they affect the behavior of fiber-reinforced concrete (FRC). Because there is a lack of specific, relevant publications in Brazil, the European standard EN14889-2:2006 was adopted as a reference to perform the characterization. Thus, an experimental plan was developed to assess the adequacy of testing procedures for the qualification of synthetic macrofibers for use in FRC. Two types of macrofibers were evaluated. The length measurement was performed using two methods: the caliper method, which is a manual measurement, and the digital image analysis method using the ImageJ software for image processing. These aforementioned methods were used to determine the diameter together with the density method, which is an indirect method that uses the developed length obtained by one of the previous methods. The statistical analyses revealed that the length results are similar regardless of the method used. However, the macrofibers must be pre-stretched to maximize the accuracy of caliper measurements. The caliper method for diameter determination has the disadvantage of underestimating the macrofiber cross-section because of the pressure applied by the load claws. In contrast, the digital image analysis method obtains the projected diameter in a single plane, which overestimate the diameter because the macrofibers are oriented with the pressure of the scanner cover. Thus, these techniques may result in false projections of the diameters that will depend on the level of torsion in the macrofibers. It was concluded that both the caliper method using previously stretched macrofibers and the digital imaging method can be used to measure length. The density method presented the best results for the diameter determination because these results were not affected by the method chosen to determine the length.