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In practical engineering construction, differences in time intervals during concrete pouring arise due to issues in concrete quality control and construction procedures, thereby affecting the mechanical and durability properties of concrete. This study conducted compressive strength tests, splitting tensile strength tests, and natural immersion tests to investigate the influence of time intervals in layered pouring on the mechanical strength and chloride ion concentration distribution of staged pouring concrete. Additionally, the study elucidated the mechanism by which pouring interval time affects the mechanical properties and resistance to chloride ion erosion of staged pouring concrete at the microstructure level. The results indicate that compared to ordinary concrete specimens, the splitting tensile strength of staged pouring concrete demonstrates a continuous decrease with increasing pouring interval time. The most significant splitting tensile strength decrease occurred at a 24 h interval. The compressive strength of staged pouring concrete initially decreases and then increases with increasing pouring interval time. At a pouring interval time of 12 h, the compressive strength of staged pouring concrete decreased the most. Results from the natural immersion tests demonstrate that chloride ion concentrations at the bonding interface and on both sides of staged pouring concrete increase continuously with the extension of pouring interval time. The chloride ion concentration at the bonding interface is consistently higher than that on both sides, and the difference between them decreases with increasing diffusion depth. The chloride ion concentration difference ΔC was proposed to evaluate the influence of bonding interface performance on chloride ion concentration, which decreases to varying degrees with increasing depth. The findings of this study can provide guidance for the research on the mechanical properties and durability of staged pouring concrete in practical engineering construction, as well as for engineering protective measures. [ABSTRACT FROM AUTHOR]

In dam engineering, the presence of cracks and crack width are important indicators for diagnosing the health of dams. The accurate measurement of cracks facilitates the safe use of dams. The manual detection of such defects is unsatisfactory in terms of cost, safety, accuracy, and the reliability of evaluation. The introduction of deep learning for crack detection can overcome these issues. However, the current deep learning algorithms possess a large volume of model parameters, high hardware requirements, and difficulty toward embedding in mobile devices such as drones. Therefore, we propose a lightweight MobileNetV2_DeepLabV3 image segmentation network. Furthermore, to prevent interference by noise, light, shadow, and other factors for long-length targets when segmenting, the atrous spatial pyramid pooling (ASPP) module parameters in the DeepLabV3+ network structure were modified, and a multifeature fusion structure was used instead of the parallel structure in ASPP, allowing the network to obtain richer crack features. We collected the images of dam cracks from different environments, established segmentation datasets, and obtained segmentation models through network training. Experiments show that the improved MobileNetV2_DeepLabV3 algorithm exhibited a higher crack segmentation accuracy than the original MobileNetV2_DeepLabV3 algorithm; the average intersection rate attained 83.23%; and the crack detail segmentation was highly accurate. Compared with other semantic segmentation networks, its training time was at least doubled, and the total parameters were reduced by more than 2 to 7 times. After extracting cracks through the semantic segmentation, we proposed to use the method of inscribed circle of crack outline to calculate the maximum width of the detected crack image and to convert it into the actual width of the crack. The maximum relative error rate was 11.22%. The results demonstrated the potential of innovative deep learning methods for dam crack detection. [ABSTRACT FROM AUTHOR]

The demand for intelligent agriculture is increasing due to the continuous impact of world food and environmental crises. Focusing on fruit detection, with the rapid development of object detection technology, it is now possible to achieve high efficiency and high accuracy in fruit detection systems. However, detecting fruit with high precision in unstructured orchard environments remains particularly challenging. Such environments, which are composed of varying illumination conditions and degrees of occlusion, can be mitigated by certain strategies. To our knowledge, this is the first time that optimization strategies used in fruit detection have been reviewed. This review aims to explore methods for improving fruit detection in complex environments. First, we describe the common types of complex backgrounds found in outdoor orchard environments. Subsequently, we divide the improvement measures into two categories: optimization before and after image sampling. Next, we compare the test results obtained before and after the application of these improved methods. Finally, we describe the future development trends of fruit detection optimization technology in complex backgrounds. We hope that this review will inspire researchers to design their optimization strategies and help explore lower-cost and more robust fruit detection systems. [ABSTRACT FROM AUTHOR]

Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny–Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software lableImg and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny–Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision ( m A P ) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny–Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection. [ABSTRACT FROM AUTHOR]

Camellia oleifera fruits are randomly distributed in an orchard, and the fruits are easily blocked or covered by leaves. In addition, the colors of leaves and fruits are alike, and flowers and fruits grow at the same time, presenting many ambiguities. The large shock force will cause flowers to fall and affect the yield. As a result, accurate positioning becomes a difficult problem for robot picking. Therefore, studying target recognition and localization of Camellia oleifera fruits in complex environments has many difficulties. In this paper, a fusion method of deep learning based on visual perception and image processing is proposed to adaptively and actively locate fruit recognition and picking points for Camellia oleifera fruits. First, to adapt to the target classification and recognition of complex scenes in the field, the parameters of the You Only Live Once v7 (YOLOv7) model were optimized and selected to achieve Camellia oleifera fruits' detection and determine the center point of the fruit recognition frame. Then, image processing and a geometric algorithm are used to process the image, segment, and determine the morphology of the fruit, extract the centroid of the outline of Camellia oleifera fruit, and then analyze the position deviation of its centroid point and the center point in the YOLO recognition frame. The frontlighting, backlight, partial occlusion, and other test conditions for the perceptual recognition processing were validated with several experiments. The results demonstrate that the precision of YOLOv7 is close to that of YOLOv5s, and the mean average precision of YOLOv7 is higher than that of YOLOv5s. For some occluded Camellia oleifera fruits, the YOLOv7 algorithm is better than the YOLOv5s algorithm, which improves the detection accuracy of Camellia oleifera fruits. The contour of Camellia oleifera fruits can be extracted entirely via image processing. The average position deviation between the centroid point of the image extraction and the center point of the YOLO recognition frame is 2.86 pixels; thus, the center point of the YOLO recognition frame is approximately considered to be consistent with the centroid point of the image extraction. [ABSTRACT FROM AUTHOR]

In order to detect different biological features and dynamic tiny targets with subtle features more accurately and efficiently and analyze the subtle differences of biological features, this paper proposes classifying and identifying the local contour edge images of biological features and different types of targets and reveals high similarities in their subtle features. Taking pigeons as objects, there is little difference in appearance between female pigeons and male pigeons. Traditional methods need to manually observe the morphology near the anus of pigeons to identify their sex or carry out chromosome examination or even molecular biological examination to achieve accurate sex identification. In this paper, a compound marker region for extracting gender features is proposed. This area has a strong correlation with the gender difference of pigeons, and its area's proportion is low, which can reduce calculation costs. A dual-weight image fusion feature enhancement algorithm based on edge detection is proposed. After the color information and contour information of the image are extracted, a new feature enhancement image is fused according to a pair of weights, and the difference between tiny features increased so as to realize the detection and identification of pigeon sex by visual methods. The results show that the detection accuracy is 98%, and the F1 value is 0.98. Compared with the original data set without any enhancement, the accuracy increased by 32% and the F1 score increased by 0.35. Experiments show that this method can achieve accurate visual sex classifications of pigeons and provide intelligent decision data for pigeon breeding. [ABSTRACT FROM AUTHOR]

The dynamic increase factor (DIF) of the concrete material strength, obtained using a split Hopkinson pressure bar (SHPB), includes structural effects that do not precisely reflect the real strain-rate effect of concrete. To further clarify the real strain-rate effects of rubberised concrete (RC), an experimental investigation regarding the dynamic compressive response of ordinary concrete (NC) and RC with three rubber contents (10%, 20%, and 30%) was performed in this study. Additionally, based on a dynamic constitutive model, i.e., the Karagozian and Case (K&C) concrete model, numerical SHPB tests were conducted using the LS-DYNA software. According to the experimental results, all parameters of the K&C model were discussed, and the damage factors were modified to satisfy the mechanical properties of RC. After validating the numerical model, it was observed that the experimental DIF included the inertial enhancement and the real DIF. Moreover, because rubber particles effectively reduce the density and improve the deformation capacity of concrete, the real strain-rate effect of RC was found to be more rate-sensitive than that of NC by analysing the radial stress distribution. In addition to lateral inertia, another external source, namely, the interface friction between the specimen and bars, which can produce lateral confinement, was further studied. It was found that interface friction significantly contributes to lateral confinement; however, as the strain rate increased, the impact generally decreased. Finally, the mechanism of the strain-rate effect of RC was clarified. [ABSTRACT FROM AUTHOR]

Prefabricated utility tunnels have drawn much attention in relation to rapid urban development. On this, how to maintain the integrity of an underground lifeline, which is subjected to unexpected fault displacement action, is a concern either from the design or the construction aspect. By applying the commercial software program ABAQUS, this paper presents a systematic numerical simulation of a prefabricated utility tunnel affected by a reverse fault. The critical parameters investigated in this study include fault displacement, burial depth, utility tunnel-soil friction coefficient, and the angle of the utility tunnel crossing the fault plane. Results of the numerical modeling revealed that: (1) both the overall structural deformation and the spliced joints deformation of the prefabricated utility tunnel increase with increasing fault displacement, which greatly reduces the waterproofing ability of the utility tunnel joints; (2) the opening displacement of the joints on the roof of the utility tunnel near the fault plane is positively correlated with burial depth, but the variation is slight; (3) the variations in utility tunnel-soil friction coefficient have little effect on the overall structural deformation and the spliced joints deformation; (4) the opening displacement of the spliced joints of the utility tunnel basically gradually increases with an increase in the crossing angle near the fault plane, which is different than when it is away from the fault plane. The main outcomes obtained from this study can provide reference for the construction of prefabricated utility tunnel in fault active area. [ABSTRACT FROM AUTHOR]

Agricultural machinery intelligence is the inevitable direction of agricultural machinery design, and the systems in these designs are important tools. In this paper, to address the problem of low processing power of traditional agricultural machinery design systems in analyzing data, such as fit, tolerance, interchangeability, and the assembly process, as well as to overcome the disadvantages of the high cost of intelligent design modules, lack of data compatibility, and inconsistency between modules, a novel agricultural machinery intelligent design system integrating image processing and knowledge reasoning is constructed. An image-processing algorithm and trigger are used to detect the feature parameters of key parts of agricultural machinery and build a virtual prototype. At the same time, a special knowledge base of agricultural machinery is constructed to analyze the test data of the virtual prototype. The results of practical application and software evaluation of third-party institutions show that the system improves the efficiency of intelligent design in key parts of agricultural machinery by approximately 20%, reduces the operation error rate of personnel by approximately 40% and the consumption of computer resources by approximately 30%, and greatly reduces the purchase cost of intelligent design systems to provide a reference for intelligent design to guide actual production. [ABSTRACT FROM AUTHOR]

For the automated robotic picking of bunch-type fruit, the strategy is to roughly determine the location of the bunches, plan the picking route from a remote location, and then locate the picking point precisely at a more appropriate, closer location. The latter can reduce the amount of information to be processed and obtain more precise and detailed features, thus improving the accuracy of the vision system. In this study, a long-close distance coordination control strategy for a litchi picking robot was proposed based on an Intel Realsense D435i camera combined with a point cloud map collected by the camera. The YOLOv5 object detection network and DBSCAN point cloud clustering method were used to determine the location of bunch fruits at a long distance to then deduce the sequence of picking. After reaching the close-distance position, the Mask RCNN instance segmentation method was used to segment the more distinctive bifurcate stems in the field of view. By processing segmentation masks, a dual reference model of "Point + Line" was proposed, which guided picking by the robotic arm. Compared with existing studies, this strategy took into account the advantages and disadvantages of depth cameras. By experimenting with the complete process, the density-clustering approach in long distance was able to classify different bunches at a closer distance, while a success rate of 88.46% was achieved during fruit-bearing branch locating. This was an exploratory work that provided a theoretical and technical reference for future research on fruit-picking robots. [ABSTRACT FROM AUTHOR]

In recent years, there has been unprecedented development of marine engineering construction in China. One such development has been the introduction of geopolymer (GP) concrete, which differs from portland cement in terms of durability and corrosion resistance in marine environments, and is also a low-carbon construction material. This study developed a low-shrinkage modified GP (MGP) concrete and researched its potential engineering applications. The research prototype was the Xigang seawall in the Guangdong Province of China. The effects of working performance, compressive strength, temperature rise, and strain of the concrete on the construction were assessed. The results showed that the autogenous shrinkage, chemical shrinkage, and drying shrinkage of the MGP paste were significantly lower than those of conventional GP concrete. The mechanism of shrinkage compensation of the MGP paste was discussed from the viewpoint of mesostructure and thermodynamics. The MGP concrete demonstrated smaller temperature rise and less difference between the internal and external temperatures than did portland cement concrete. Therefore, it featured a lower risk of cracking and was found to be more suitable than portland cement concrete for the rapid construction of seawalls. [ABSTRACT FROM AUTHOR]

In this research, the plate embedded parts and grooved embedded parts reinforced concrete structures were investigated. Two types of plate embedded parts and three types of grooved embedded parts experienced coating treatment to enable sustainable function. Later, the ultimate failure capacity by bending experiments was conducted and compared with the theoretically calculated results. Moreover, three grooved embedded parts were simulated by ABAQUS to compare the results with the experimental exploration results, which was in close agreement with the theoretically calculated results and finite element analysis results. The result indicated that the failure modes of the embedded specimens under the five working conditions are all concrete vertebral failure. The plate-type embedded components were proved to exhibit higher ultimate bearing capacity than the grooved embedded parts. Moreover, the flexural and shear capacity of these five types of embedded parts has not been fully developed. The ultimate flexural and shear capacity of these five types of embedded parts could be further explored by adjusting the higher concrete grade. [ABSTRACT FROM AUTHOR]

Accurate and reliable fruit detection in the orchard environment is an important step for yield estimation and robotic harvesting. However, the existing detection methods often target large and relatively sparse fruits, but they cannot provide a good solution for small and densely distributed fruits. This paper proposes a YOLOv3-Litchi model based on YOLOv3 to detect densely distributed litchi fruits in large visual scenes. We adjusted the prediction scale and reduced the network layer to improve the detection ability of small and dense litchi fruits and ensure the detection speed. From flowering to 50 days after maturity, we collected a total of 266 images, including 16,000 fruits, and then used them to construct the litchi dataset. Then, the k-means++ algorithm is used to cluster the bounding boxes in the labeled data to determine the priori box size suitable for litchi detection. We trained an improved YOLOv3-Litchi model, tested its litchi detection performance, and compared YOLOv3-Litchi with YOLOv2, YOLOv3, and Faster R-CNN on the actual detection effect of litchi and used the F1 value and the average detection time as the assessed value. The test results show that the F1 of YOLOv3-Litchi is higher than that of YOLOv2 algorithm 0.1, higher than that of YOLOv3 algorithm 0.08, and higher than that of Faster R-CNN algorithm 0.05; the average detection time of YOLOv3-Litchi is 29.44 ms faster than that of YOLOv2 algorithm, 19.56 ms faster than that of YOLOv3 algorithm ms, and 607.06 ms faster than that of Faster R-CNN algorithm. And the detection speed of the improved model is faster. The proposed model remits optimal detection performance for small and dense fruits. The work presented here may provide a reference for further study on fruit-detection methods in natural environments. [ABSTRACT FROM AUTHOR]

A four-ocular vision system is proposed for the three-dimensional (3D) reconstruction of large-scale concrete-filled steel tube (CFST) under complex testing conditions. These measurements are vitally important for evaluating the seismic performance and 3D deformation of large-scale specimens. A four-ocular vision system is constructed to sample the large-scale CFST; then point cloud acquisition, point cloud filtering, and point cloud stitching algorithms are applied to obtain a 3D point cloud of the specimen surface. A point cloud correction algorithm based on geometric features and a deep learning algorithm are utilized, respectively, to correct the coordinates of the stitched point cloud. This enhances the vision measurement accuracy in complex environments and therefore yields a higher-accuracy 3D model for the purposes of real-time complex surface monitoring. The performance indicators of the two algorithms are evaluated on actual tasks. The cross-sectional diameters at specific heights in the reconstructed models are calculated and compared against laser rangefinder data to test the performance of the proposed algorithms. A visual tracking test on a CFST under cyclic loading shows that the reconstructed output well reflects the complex 3D surface after correction and meets the requirements for dynamic monitoring. The proposed methodology is applicable to complex environments featuring dynamic movement, mechanical vibration, and continuously changing features. [ABSTRACT FROM AUTHOR]

The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described. [ABSTRACT FROM AUTHOR]

This paper proposes a novel technique for fruit detection in natural environments which is applicable in automatic harvesting robots, yield estimation systems and quality monitoring systems. As most color-based techniques are highly sensitive to illumination changes and low contrasts between fruits and leaves, the proposed technique, conversely, is based on contour information. Firstly, a discriminative shape descriptor is derived to represent geometrical properties of arbitrary fragment, and applied to a bidirectional partial shape matching to detect sub-fragments of interest that match parts of a reference contour. Then, a novel probabilistic Hough transform is developed to aggregate these sub-fragments for obtaining fruit candidates. Finally, all fruit candidates are verified by a support vector machine classifier trained on color and texture features. Citrus, tomato, pumpkin, bitter gourd, towel gourd and mango datasets were provided. Experiments on these datasets demonstrated that the proposed approach was competitive for detecting most type of fruits, such as green, orange, circular and non-circular, in natural environments. [ABSTRACT FROM AUTHOR]

A novel detection algorithm based on color, depth, and shape information is proposed for detecting spherical or cylindrical fruits on plants in natural environments and thus guiding harvesting robots to pick them automatically. A probabilistic image segmentation method is first presented to segment a red–green–blue image as a binary mask. Multiplied by this mask, a filtered depth image is obtained. Region growing, a region-based image segmentation method, is then applied to group the depth image into multiple clusters. Each cluster represents a fruit, leaf, or branch that is later transformed into a point cloud. Next, a 3D shape detection method based on M-estimator sample consensus, a model parameter estimator, is employed to detect potential fruits from each point cloud. Finally, an angle/color/shape-based global point cloud descriptor (GPCD) is developed to extract a feature vector for an entire point cloud, and a support vector machine classifier trained on the GPCD features is used to exclude false positives. Pepper, eggplant, and guava datasets were captured in the field. For the pepper, eggplant, and guava datasets, the detection precision was 0.864, 0.886, and 0.888, and the recall was 0.889, 0.762, and 0.812, respectively. Experiments revealed that the proposed algorithm was universal and robust and hence applicable to an agricultural harvesting robot. [ABSTRACT FROM AUTHOR]

Counting grape berries and measuring their size can provide accurate data for robot picking behavior decision-making, yield estimation, and quality evaluation. When grapes are picked, there is a strong uncertainty in the external environment and the shape of the grapes. Counting grape berries and measuring berry size are challenging tasks. Computer vision has made a huge breakthrough in this field. Although the detection method of grape berries based on 3D point cloud information relies on scanning equipment to estimate the number and yield of grape berries, the detection method is difficult to generalize. Grape berry detection based on 2D images is an effective method to solve this problem. However, it is difficult for traditional algorithms to accurately measure the berry size and other parameters, and there is still the problem of the low robustness of berry counting. In response to the above problems, we propose a grape berry detection method based on edge image processing and geometric morphology. The edge contour search and the corner detection algorithm are introduced to detect the concave point position of the berry edge contour extracted by the Canny algorithm to obtain the best contour segment. To correctly obtain the edge contour information of each berry and reduce the error grouping of contour segments, this paper proposes an algorithm for combining contour segments based on clustering search strategy and rotation direction determination, which realizes the correct reorganization of the segmented contour segments, to achieve an accurate calculation of the number of berries and an accurate measurement of their size. The experimental results prove that our proposed method has an average accuracy of 87.76% for the detection of the concave points of the edge contours of different types of grapes, which can achieve a good edge contour segmentation. The average accuracy of the detection of the number of grapes berries in this paper is 91.42%, which is 4.75% higher than that of the Hough transform. The average error between the measured berry size and the actual berry size is 2.30 mm, and the maximum error is 5.62 mm, which is within a reasonable range. The results prove that the method proposed in this paper is robust enough to detect different types of grape berries. [ABSTRACT FROM AUTHOR]

Fruit detection in real outdoor conditions is necessary for automatic guava harvesting, and the branch-dependent pose of fruits is also crucial to guide a robot to approach and detach the target fruit without colliding with its mother branch. To conduct automatic, collision-free picking, this study investigates a fruit detection and pose estimation method by using a low-cost red–green–blue–depth (RGB-D) sensor. A state-of-the-art fully convolutional network is first deployed to segment the RGB image to output a fruit and branch binary map. Based on the fruit binary map and RGB-D depth image, Euclidean clustering is then applied to group the point cloud into a set of individual fruits. Next, a multiple three-dimensional (3D) line-segments detection method is developed to reconstruct the segmented branches. Finally, the 3D pose of the fruit is estimated using its center position and nearest branch information. A dataset was acquired in an outdoor orchard to evaluate the performance of the proposed method. Quantitative experiments showed that the precision and recall of guava fruit detection were 0.983 and 0.948, respectively; the 3D pose error was 23.43° ± 14.18°; and the execution time per fruit was 0.565 s. The results demonstrate that the developed method can be applied to a guava-harvesting robot. [ABSTRACT FROM AUTHOR]