Search Results (73)

More recently, artificial intelligence (AI)-generated content (AIGC) is fundamentally transforming multiple sectors, including materials discovery, healthcare, education, scientific research, and industrial manufacturing. As for the complexities and challenges of intelligent robot design, AIGC has the potential to offer a new paradigm, assisting in conceptual and technical design, functional module design, and the training of the perception ability to accelerate prototyping. Taking the design of an apple-harvesting robot, for example, we demonstrate a basic framework of the AIGC-assisted robot design methodology, leveraging the generation capabilities of available multimodal large language models, as well as the human intervention to alleviate AI hallucination and hidden risks. Second, we study the enhancement effect on the robot perception system using the generated apple images based on the large vision-language models to expand the actual apple images dataset. Further, an apple-harvesting robot prototype based on an AIGC-aided design is demonstrated and a pick-up experiment in a simulated scene indicates that it achieves a harvesting success rate of 92.2% and good terrain traversability with a maximum climbing angle of 32°. According to the tentative research, although not an autonomous design agent, the AIGC-driven design workflow can alleviate the significant complexities and challenges of intelligent robot design, especially for beginners or young engineers. Full article

Tea disease detection is of great significance to the tea industry. In order to solve the problems such as mutual occlusion of leaves, light disturbance, and small lesion area under complex background, YOLO-SSM, a tea disease detection model, was proposed in this paper. The model introduces the SSPDConv convolution module in the backbone of YOLOv8 to enhance the global information perception of the model under complex backgrounds; a new ESPPFCSPC module is proposed to replace the original spatial pyramid pool SPPF module, which optimizes the multi-scale feature expression; and the MPDIoU loss function is introduced to optimize the problem that the original CIoU is insensitive to the change of target size, and the positioning ability of small targets is improved. Finally, the map values of 89.7% and 68.5% were obtained on a self-made tea data set and a public tea disease data set, which were improved by 3.9% and 4.3%, respectively, compared with the original benchmark model, and the reasoning speed of the model was 164.3 fps. Experimental results show that the proposed YOLO-SSM algorithm has obvious advantages in accuracy and model complexity and can provide reliable theoretical support for efficient and accurate detection and identification of tea leaf diseases in natural scenes. Full article

Computer Vision (CV) has proven to be a powerful tool for automation in agri-food industrial processes, offering high-precision solutions tailored to specific working conditions. Recent advancements in Artificial Neural Networks (ANNs) have revolutionized CV applications, enabling systems to autonomously learn and optimize tasks. However, ANN-based approaches often require complex development and lengthy training periods, making their implementation a challenge. In this study, we explore the use of the Segment Anything Model (SAM), a pre-trained neural network developed by META AI in 2023, as an alternative for industrial segmentation tasks in the table olive (Olea europaea L.) processing industry. SAM’s ability to segment objects regardless of scene composition makes it a promising tool to improve the efficiency of olive pitting machines (DRRs). These machines, widely employed in industrial processing, frequently experience mechanical inefficiencies, including the “boat error,” which arises when olives are improperly oriented, leading to defective pitting and pit splinter contamination. Our approach integrates SAM into n CV workflow to diagnose and quantify boat errors without designing or training an additional task-specific ANN. By analyzing the segmented images, we can determine both the percentage of boat errors and the size distribution of olives during transport. The results validate SAM as a feasible option for industrial segmentation, offering a simpler and more accessible solution compared to traditional ANN-based methods. Moreover, our statistical analysis reveals that improper calibration—manifested as size deviations from the nominal value—does not significantly increase boat error rates. This finding supports the adoption of complementary CV technologies to enhance olive pitting efficiency. Future work could investigate real-time integration and the combination of CV with electromechanical correction systems to fully automate and optimize the pitting process. Full article

The strategic prioritization of inefficient industrial land (IIL) redevelopment is critical for directing capital allocation toward sustainable urban regeneration. However, current redevelopment prioritization suffers from inefficient identification of IIL and ambiguous characterization of redevelopment potential, which hinders the efficiency of land resource allocation. To address these challenges, this study develops an AI-driven redevelopment prioritization framework for identifying IIL, evaluating redevelopment potential, and establishing implementation priorities. For land identification we propose an improved YOLOv11 model with an AdditiveBlock module to enhance feature extraction in complex street view scenes, achieving an 80.1% mAP on a self-built dataset of abandoned industrial buildings. On this basis, a redevelopment potential evaluation index system is constructed based on the necessity, maturity, and urgency of redevelopment, and the Particle Swarm Optimization-Projection Pursuit (PSO-PP) model is introduced to objectively evaluate redevelopment potential by adaptively reducing the reliance on expert judgment. Subsequently, the redevelopment priorities were classified according to the calculated potential values. The proposed framework is empirically tested in the central urban area of Ningbo City, China, where inefficient industrial land is successfully identified and redevelopment priority is categorized into near-term, medium-term, and long-term stages. Results show that the framework integrating computer vision and machine learning technology can effectively provide decision support for the redevelopment of IIL and offer a new method for promoting the smart growth of urban space. Full article

As oil and gas exploration has deepened, the complexity and risk of well repair operations has increased, and the traditional description methods based on text and charts have limitations in accuracy and efficiency. Therefore, this study proposes a well repair scene description method based on visual language technology and a cross-modal coupling prompt enhancement mechanism. The research first analyzes the characteristics of well repair scenes and clarifies the key information requirements. Then, a set of prompt-enhanced visual language models is designed, which can automatically extract key information from well site images and generate structured natural language descriptions. Experiments show that this method significantly improves the accuracy of target recognition (from 0.7068 to 0.8002) and the quality of text generation (the perplexity drops from 3414.88 to 74.96). Moreover, this method is universal and scalable, and it can be applied to similar complex scene description tasks, providing new ideas for the application of well repair operations and visual language technology in the industrial field. In the future, the model performance will be further optimized, and application scenarios will be expanded to contribute to the development of oil and gas exploration. Full article

Environmental control based on growth stage is critical for enhancing the yield and quality of industrially cultivated Pleurotus pulmonarius. Challenges such as scene complexity and overlapping mushroom clusters can impact the accuracy of growth stage detection and target segmentation. This study introduces a lightweight method called the real-time detection model for the growth stages of P. pulmonarius (GSP-RTMDet). A spatial pyramid pooling fast network with simple parameter-free attention (SPPF-SAM) was proposed, which enhances the backbone’s capability to extract key feature information. Additionally, it features an interactive attention mechanism between spatial and channel dimensions to build a cross-stage partial spatial group-wise enhance network (CSP-SGE), improving the feature fusion capability of the neck. The class-aware adaptive feature enhancement (CARAFE) upsampling module is utilized to enhance instance segmentation performance. This study innovatively fusions the improved methods, enhancing the feature representation and the accuracy of masks. By lightweight model design, it achieves real-time growth stage detection of P. pulmonarius and accurate instance segmentation, forming the foundation of an environmental control strategy. Model evaluations reveal that GSP-RTMDet-S achieves an optimal balance between accuracy and speed, with a bounding box mean average precision (bbox mAP) and a segmentation mAP (segm mAP) of 96.40% and 93.70% on the test set, marking improvements of 2.20% and 1.70% over the baseline. Moreover, it boosts inference speed to 39.58 images per second. This method enhances detection and segmentation outcomes in real-world environments of P. pulmonarius houses, offering a more accurate and efficient growth stage perception solution for environmental control. Full article

Laminated panels are widely used in industry, and their quality inspection has traditionally relied on manual labor, which is time-consuming and prone to errors. Automated detection can significantly improve efficiency and reduce human error. With prior knowledge, object detectors focus on updating model structures to improve performance. Despite initial success, most methods become increasingly complex and time-consuming for industrial applications while also neglecting the object distributions in the industrial dataset, especially in the context of industrial laminated panels. All these issues have led to missed and false detections of objects in this scene. We therefore propose a prior-enhanced resolution-extended object detector framework (PERE) for industrial scenarios to solve these issues while enhancing detection accuracy and efficiency. PERE explores the spatial connection of objects and seeks the latent information within the process of forward propagation. PERE introduces the prior-enhanced network (MRPE) and the resolution-extended network (REN) to replace initial modules in one-stage object detectors. MRPE extracts prior knowledge from the spatial distribution of objects in industrial scenes, migrating false detections caused by feature similarities. REN incorporates super-resolution information during the upsampling process to minimize the risk of missing tiny targets. At the same time, we have built a new dataset SPI for studying this topic. Comprehensive experiments show that PERE significantly improves efficiency and performance in object detection within industrial scenes. Full article

Common single-line 2D LiDAR sensors and cameras have become core components in the field of robotic perception due to their low cost, compact size, and practicality. However, during the data fusion process, the randomness and complexity of real industrial scenes pose challenges. Traditional calibration methods for LiDAR and cameras often rely on precise targets and can accumulate errors, leading to significant limitations. Additionally, the semantic fusion of LiDAR and camera data typically requires extensive projection calculations, complex clustering algorithms, or sophisticated data fusion techniques, resulting in low real-time performance when handling large volumes of data points in dynamic environments. To address these issues, this paper proposes a semantic fusion algorithm for LiDAR and camera data based on contour and inverse projection. The method has two remarkable features: (1) Combined with the ellipse extraction algorithm of the arc support line segment, a LiDAR and camera calibration algorithm based on various regular shapes of an environmental target is proposed, which improves the adaptability of the calibration algorithm to the environment. (2) This paper proposes a semantic segmentation algorithm based on the inverse projection of target contours. It is specifically designed to be versatile and applicable to both linear and arc features, significantly broadening the range of features that can be utilized in various tasks. This flexibility is a key advantage, as it allows the method to adapt to a wider variety of real-world scenarios where both types of features are commonly encountered. Compared with existing LiDAR point cloud semantic segmentation methods, this algorithm eliminates the need for complex clustering algorithms, data fusion techniques, and extensive laser point reprojection calculations. When handling a large number of laser points, the proposed method requires only one or two inverse projections of the contour to filter the range of laser points that intersect with specific targets. This approach enhances both the accuracy of point cloud searches and the speed of semantic processing. Finally, the validity of the semantic fusion algorithm is proven by field experiments. Full article

In order to solve the problem of imaging quality of industrial cameras for low-light and large dynamic scenes in many fields, such as smart city and target recognition, this study focuses on overcoming two core challenges: first, the loss of image details due to the significant difference in light distribution in complex scenes, and second, the coexistence of dark and light areas under the constraints of the limited dynamic range of a camera. To this end, we propose a low-light high-dynamic-range image enhancement method based on dynamic weights and pyramid fusion. In order to verify the effectiveness of the method, experimental data covering full-time scenes are acquired based on an image acquisition platform built in the laboratory, and a comprehensive evaluation system combining subjective visual assessment and objective indicators is constructed. The experimental results show that, in a multi-temporal fusion task, this study’s method performs well in multiple key indicators such as information entropy (EN), average gradient (AG), edge intensity (EI), and spatial frequency (SF), making it especially suitable for imaging in low-light and high-dynamic-range environments. Specifically in localized low-light high-dynamic-range regions, compared with the best-performing comparison method, the information entropy indexes of this study’s method are improved by 4.88% and 6.09%, which fully verifies its advantages in detail restoration. The research results provide a technical solution with all-day adaptive capability for low-cost and lightweight surveillance equipment, such as intelligent transportation systems and remote sensing security systems, which has broad application prospects. Full article

The mushroom farming industry struggles to automate harvesting due to limited large-scale annotated datasets and the complex growth patterns of mushrooms, which complicate detection, segmentation, and pose estimation. To address this, we introduce a synthetic dataset with 40,000 unique scenes of white Agaricus bisporus and brown baby bella mushrooms, capturing realistic variations in quantity, position, orientation, and growth stages. Our two-stage pose estimation pipeline combines 2D object detection and instance segmentation with a 3D point cloud-based pose estimation network using a Point Transformer. By employing a continuous 6D rotation representation and a geodesic loss, our method ensures precise rotation predictions. Experiments show that processing point clouds with 1024 points and the 6D Gram–Schmidt rotation representation yields optimal results, achieving an average rotational error of $1.67°$ on synthetic data, surpassing current state-of-the-art methods in mushroom pose estimation. The model, further, generalizes well to real-world data, attaining a mean angle difference of $3.68°$ on a subset of the M18K dataset with ground-truth annotations. This approach aims to drive automation in harvesting, growth monitoring, and quality assessment in the mushroom industry. Full article

Conveyor belt deviation is one of the most common failures in belt conveyors. To address issues such as the high computational complexity, large number of parameters, long inference time, and difficulty in feature extraction of existing conveyor belt deviation detection models, we propose a GES-YOLO algorithm for detecting deviation in mining belt conveyors, based on an improved YOLOv8s model. The core of this algorithm is to enhance the model’s ability to extract features in complex scenarios, thereby improving the detection efficiency. Specifically, to improve real-time detection capabilities, we introduce the Groupwise Separable Convolution (GSConv) module. Additionally, by analyzing scene features, we remove the large object detection layer, which enhances the detection speed while maintaining the feature extraction capability. Furthermore, to strengthen feature perception under low-light conditions, we introduce the Efficient Multi-Scale Attention Mechanism (EMA), allowing the model to obtain more robust features. Finally, to improve the detection capability for small objects such as conveyor rollers, we introduce the Scaled Intersection over Union (SIoU) loss function, enabling the algorithm to sensitively detect rollers and provide a precise localization for deviation detection. The experimental results show that the GES-YOLO significantly improves the detection performance in complex environments such as high-noise and low-illumination conditions in coal mines. Compared to the baseline YOLOv8s model, GES-YOLO’s mAP@0.5 and mAP@0.5:0.95 increase by 1.5% and 2.3%, respectively, while the model’s parameter count and computational complexity decrease by 38.2% and 10.5%, respectively. The Frames Per Second (FPS) of the average detection speed reaches 63.62. This demonstrates that GES-YOLO achieves a good balance between detection accuracy and inference speed, with excellent accuracy, robustness, and industrial application potential. Full article

This paper introduces an efficient localization algorithm for robotic systems, utilizing deep learning to identify and exploit natural fiduciary patterns within the environment. Diverging from conventional localization techniques that depend on artificial markers, this method capitalizes on the inherent environmental features to enhance both accuracy and computational efficiency. By integrating advanced deep learning frameworks with natural scene analysis, the proposed algorithm facilitates robust, real-time localization in dynamic and unstructured settings. The resulting approach offers significant improvements in adaptability, precision, and operational efficiency, representing a substantial contribution to the field of autonomous robotics. We are aiming at analyzing an automotive manufacturing scenario to achieve robotic localization related to a moving target. To work with a simpler and more accessible scenario we have chosen a demonstrative context consisting of a laboratory wall containing some elements. This paper will focus on the first part of the case study, with a continuation planned for future work. It will demonstrate a scenario in which a camera is mounted on a robot, capturing images of the underside of a car (which we assume to be represented by a gray painted surface with specific elements to be described in Materials and Methods). These images are processed by a convolutional neural network (CNN), designed to detect the most distinctive features of the environment. The extracted information is crucial, as the identified characteristic areas will serve as reference points for the real-time localization of the industrial robot. In this work, we have demonstrated the potential of leveraging natural fiduciary patterns for efficient and accurate robot localization. By utilizing deep learning, specifically convolutional neural networks. The experimental results suggest that this approach is not only feasible but also scalable across a wide range of applications, including industrial automation autonomous vehicles, and aerospace navigation. As robots increasingly operate in environments where computational efficiency and adaptability are paramount, our methodology offers a viable solution to enhance localization without compromising accuracy or speed. The proposal of an algorithm that enables the application of the proposed method for natural fiduciary patterns based on neural networks to more complex scenarios is highlighted, along with the efficiency of the method for robot localization compared to others. Full article

The trajectory planning of manipulators plays a crucial role in industrial applications. This importance is particularly pronounced when manipulators operate in environments filled with obstacles, where devising paths to navigate around obstacles becomes a pressing concern. This study focuses on the environment of frame obstacles in industrial scenes. At present, many obstacle avoidance trajectory planning algorithms struggle to strike a balance among trajectory length, generation time, and algorithm complexity. This study aims to generate path points for manipulators in an environment with obstacles, and the trajectory for these manipulators is planned. The search direction adaptive RRT*Connect (SDA-RRT*Connect) method is proposed to address this problem, which adaptively adjusts the search direction during the search process of RRT*Connect. In addition, we design a path process method to reduce the length of the path and increase its smoothness. As shown in experiments, the proposed method shows improved performances with respect to path length, algorithm complexity, and generation time, compared to traditional path planning methods. On average, the configuration space’s path length and the time of generation are reduced by 38.7% and 57.4%, respectively. Furthermore, the polynomial curve trajectory of the manipulator was planned via a PSO algorithm, which optimized the running time of the manipulator. According to the experimental results, the proposed method costs less time during the manipulator’s traveling process with respect to other comparative methods. The average reduction in running time is 45.2%. Full article

The surface texture of objects in industrial scenes is complex and diverse, and the characteristics of surface defects are often very similar to the surrounding environment and texture background, so it is difficult to accurately detect the defect area. However, when deep learning technology is used to detect complex texture surface defects, the detection accuracy is not high, due to the lack of large-scale pixel-level label datasets. Therefore, a defect detection model Siamese-RCNet for complex texture surface with a small number of annotations is proposed. The Cascade R-CNN target detection network is used as the basic framework, making full use of unlabeled image feature information, and fusing the nonlinear relationship learning ability of Siamese network and the feature extraction ability of the Res2Net backbone network to more effectively capture the subtle features of complex texture surface defects. The image difference measurement method is used to calculate the similarity between different images, and the attention module is constructed to weight the feature map of the feature extraction pyramid, so that the model can focus more on the defect area and suppress the influence of complex background texture area, so as to improve the accuracy of detection. To verify the effectiveness of the Siamese-RCNet model, a series of experiments were carried out on the DAGM2007 dataset of weakly supervised learning texture surface defects for industrial optical inspection. The results show that even if only 20% of the labeled datasets are used, the mAP@0.5 of the Siamese-RCNet model can still reach 96.9%. Compared with the traditional Cascade R-CNN and Faster R-CNN target detection networks, the Siamese-RCNet model has high accuracy, can reduce the workload of manual labeling, and provides strong support for practical applications. Full article

With the urgent need of the industry and the continuous development of artificial intelligence, research into intelligent excavators has achieved certain progress. However, intelligent excavators often face strong vibrations, dense dust, and complex objectives. These have brought severe challenges to environmental perception, and are important research difficulties that must be overcome in realizing the practical engineering applications of intelligent excavators. Many researchers have studied these problems in reducing vibration and dust noise for light detection and ranging (LiDAR) scanners, multi-sensor information fusion, and the segmentation and recognition of 3D scenes. This paper reviews the research status of these key technologies and discusses their development trends. Full article