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Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and administration make symmetric encryption difficult. The purpose of this paper is to address these concerns, the novel hybrid partial differential elliptical Rubik’s cube algorithm is developed in this study as an asymmetric image encryption approach. This novel algorithm generates a random weighted matrix, and uses the masking method on image pixels with Rubik’s cube principle. Security analysis has been conducted, it enhances and increases the reliability of the proposed algorithm against a variety of attacks including statistical and differential attacks.

In this light, a differential elliptical model is designed with two phases for image encryption and decryption. A modified image is achieved by rotating and mixing intensities of rows and columns with a masking matrix derived from the key generation technique using a unique approach based on the elliptic curve and Rubik’s cube principle.

To evaluate the security level, the proposed algorithm is tested with statistical and differential attacks on a different set of test images with peak signal-to-noise ratio, unified average changed intensity and number of pixel change rate performance metrics. These results proved that the proposed image encryption method is completely reliable and enhances image security during transmission.

The elliptic curve–based encryption is hard to break by hackers and adding a Rubik’s cube principle makes it even more complex and nearly impossible to decode. The proposed method provides reduced key size.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting.

A real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions.

The localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements.

Most crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.

Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.

The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.

The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.

Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a prominent competitive tool for destinations. Tourists' loyalty manifests itself in recommendation of the destination to others, repeat visit of the destination and willingness to revisit the destination. Although a plethora of studies have tried to define models to show the relation between loyalty and the antecedent factors leading up to it, few of them have tried to integrate these models with mathematical approaches for better understanding of loyalty behavior. The purpose of this paper is to integrate a tourist destination model with Bayesian Network in order to predict the behaviour of destination loyalty and its antecedent factors.

This paper has developed a probability model by the integration of a destination loyalty model with a Bayesian network (BN) which enables to predict and analyze the behavior of loyalty and its influential factors. To demonstrate the application of this framework, Tehran, the capital of Iran, was chosen as a destination case study.

The outcome of this research will assist in identifying the weak key points in the tourist destination area for giving insights to the marketers, businesses and policy makers for making better decisions related to destination loyalty. In the analysis process, the most influential factors were recognized as the travel environment image, natural/historical attractions and, with a lower degree, infrastructure image which help the decision maker to detect and reinforce the weak factors and put more effort in focusing on improving the necessary parts rather than the irrelevant parts.

The research identified all critical factors that have the most influence on destination loyalty while driving the associate uncertainty which is significant for the tourism industry. This resulted in better decision-making which is used to identify the impact of tourism destination loyalty.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.