Search Results (791)

Aluminum is extensively utilized in the aerospace, aviation, automotive, and other industries. The presence of surface defects on aluminum has a significant impact on product quality. However, traditional detection methods fail to meet the efficiency and accuracy requirements of industrial practices. In this study, we propose an innovative aluminum surface defect detection method based on an optimized two-stage Faster R-CNN network. A 2D camera serves as the image sensor, capturing high-resolution images in real time. Optimized lighting and focus ensure that defect features are clearly visible. After preprocessing, the images are fed into a deep learning network incorporated with a multi-scale feature pyramid structure, which effectively enhances defect recognition accuracy by integrating high-level semantic information with location details. Additionally, we introduced an optimized Convolutional Block Attention Module (CBAM) to further enhance network efficiency. Furthermore, we employed the genetic K-means algorithm to optimize prior region selection, and a lightweight Ghost model to reduce network complexity by 14.3%, demonstrating the superior performance of the Ghost model in terms of loss function optimization during training and validation as well as in terms of detection accuracy, speed, and stability. The network was trained on a dataset of 3200 images captured by the image sensor, split in an 8:1:1 ratio for training, validation, and testing, respectively. The experimental results show a mean Average Precision (mAP) of 94.25%, with individual Average Precision (AP) values exceeding 80%, meeting industrial standards for defect detection. Full article

Recently proposed direct position determination (DPD) methods have garnered considerable interest in passive localization due to their excellent positioning accuracy. However, in multiple-target environments, error locations generated by wrong associations between different targets and arrays, called ghosts, may lead to incorrect estimations of the targets, reducing positioning accuracy. To address this, we propose a ghost discrimination method for broadband DPD that exploits the frequency structure differences between various targets. In the frequency coloring strategy proposed in this study, different RGB values are assigned to the spatial spectrum of different frequencies. Then, an RGB color spatial spectrum reflecting the different frequency structures of the signals is formed, which effectively distinguishes between real targets and ghosts visually and enhances multi-target localization accuracy. The probability of correctly distinguishing between targets and ghosts in the proposed method is evaluated using simulation results. It can effectively distinguish multiple targets even at a low SNR level, a significant improvement compared with the original DPD. Furthermore, the SwellEx-96 shallow-water experimental data set is utilized to demonstrate the effectiveness of the proposed method. Full article

Gravel is the most frequently used material in infrastructure construction. However, the irregular shape of the gravel pile makes it challenging for the loader to predict a stable shoveling position, which can easily result in partial collapse or a complete landslide, thereby posing a serious threat to the equipment. In view of the imperfect method of determining the shoveling position of the pile by the current unmanned loader and the high hardware requirements for the deployment of the identification model, this paper first establishes a mathematical model of the loader, and preliminarily determines the influence of the concave and convex edges of the gravel pile on the shoveling position selection through discrete element joint simulation; secondly, the influence of the pile with different edge curvatures on the loader operation process is analyzed in the simulation software, and the radar map is used to further identify the superior position features; finally, Ghost Net is used as the backbone network, the RFB module is introduced into the Backbone, and the CBAM attention mechanism is integrated into the C3 module to identify the lightweight YOLOv5s shoveling position. Discrete element analysis and a lightweight network model were used in the above study to find the safest and most effective shoveling positions. During the test that mimicked how the loader would actually shovel, the number of parameters in the improved model was cut down to 32.5% of the original, the number of calculations was cut down to about 55.2% of the original, and the average accuracy of finding the shoveling position of the gravel pile reached 98%. Full article

Remote sensing images play a vital role in domains including environmental monitoring, agriculture, and autonomous driving. However, the detection of targets in remote sensing images remains a challenging task. This study introduces innovative methods to enhance feature extraction, feature fusion, and model optimization. The Adaptive Selective Feature Enhancement Module (AFEM) dynamically adjusts feature weights using GhostModule and sigmoid functions, thereby enhancing the accuracy of small target detection. Moreover, the Adaptive Multi-scale Convolution Kernel Feature Fusion Module (AKSFFM) enhances feature fusion through multi-scale convolution operations and attention weight learning mechanisms. Moreover, our proposed ARSOD-YOLO optimized the network architecture, component modules, and loss functions based on YOLOv8, enhancing outstanding small target detection capabilities while preserving model efficiency. We conducted experiments on the VEDAI and AI-TOD datasets, showcasing the excellent performance of ARSOD-YOLO. Our algorithm achieved an mAP50 of 74.3% on the VEDAI dataset, surpassing the YOLOv8 baseline by 3.1%. Similarly, on the AI-TOD dataset, the mAP50 reached 47.8%, exceeding the baseline network by 6.1%. Full article

Hitherto unnoticed similarities between two short stories by Gustav Meyrink and two of the most renowned and widely read ghost stories of M.R. James are detailed through comparative literary analysis. Specifically, one early occult horror tale of Meyrink, The Plants of Dr. Cinderella (1905), shows no less than about 15 congruences beneath the plot level (concerning specific story requisites) with M.R. James’s ‘Oh, Whistle, and I’ll Come to You, My Lad’ (1904), as does, to the same extent, a later, widely known Meyrink tale (The Cardinal Napellus, 1914) vis-à-vis M.R. James’s Mr Humphreys and His Inheritance (1911). Although direct, conclusive evidence is unavailable, a nexus of circumstantial evidence, building on extensive biographical and bibliographical inquiries, convergently attests to these assumed literary influences on Meyrink: for both cases, the chronology is intact and thus possible; Meyrink was expertly fluent in English and well-connected to England and English literature; and, these borrowings are reminiscent of other, already known originality issues surrounding Meyrink’s work. Altogether, these new discoveries shed fresh light on idiosyncrasies of Meyrink’s creative process, imagination, and literary production; on his still under-researched literary inspirational sources; as well as on the early reception of M.R. James’s ghostly fiction beyond the anglophone sphere. Full article

Cantonese morning tea drinking is a cherished intangible cultural heritage in the Guangdong–Hong Kong–Macao Greater Bay Area in China and embodies Guangdong’s culinary tradition. In the digital era, innovative design techniques and digital technology have been applied to preserve and promote this tradition. To enhance heritage education, we designed an augmented reality device for Cantonese morning tea using Pepper’s Ghost technique. Employing field investigations, literature research, and experiments, we showcased Cantonese morning tea drinking. For further developments, affordable materials and professional photos are essential for constructing the model. Full article

The die bonding process is one of the most critical steps in the front-end semiconductor packaging process, as it significantly affects the yield of the entire IC packaging process. This research aims to find an efficient, intelligent vision detection model to identify whether each chip correctly adheres to the IC substrate; by utilizing the detection model to classify the type of defects occurring in the die bond images, the engineers can analyze the leading causes, enabling timely adjustments to key machine parameters in real-time, improving the yield of the die bond process, and significantly reducing manufacturing cost losses. This study proposes the lightweight Yolov7-tiny model using Depthwise-Separable and Ghost Convolutions and Sigmoid Linear Unit with β parameter (DSGβSI-Yolov7-tiny), which we can apply for real-time and efficient detection and prediction of die bond quality. The model achieves a maximum FPS of 192.3, a precision of 99.1%, and an F1-score of 0.97. Therefore, the performance of the proposed DSGβSI-Yolov7-tiny model outperforms other methods. Full article

Ginseng is a common medicinal herb with high value due to its unique medicinal properties. Traditional methods for classifying ginseng rely heavily on manual judgment, which is time-consuming and subjective. In contrast, deep learning methods can objectively learn the features of ginseng, saving both labor and time. This experiment proposes a ginseng-grade classification model based on an improved ResNeXt50 model. First, each convolutional layer in the Bottleneck structure is replaced with the corresponding Ghost module, reducing the model’s computational complexity and parameter count without compromising performance. Second, the SE attention mechanism is added to the model, allowing it to capture feature information more accurately and precisely. Next, the ELU activation function replaces the original ReLU activation function. Then, the dataset is augmented and divided into four categories for model training. A model suitable for ginseng grade classification was obtained through experimentation. Compared with classic convolutional neural network models ResNet50, AlexNet, iResNet, and EfficientNet_v2_s, the accuracy improved by 10.22%, 5.92%, 4.63%, and 3.4%, respectively. The proposed model achieved the best results, with a validation accuracy of up to 93.14% and a loss value as low as 0.105. Experiments have shown that this method is effective in recognition and can be used for ginseng grade classification research. Full article

Lily is one of the world’s important ornamental flowers. Potted Asiatic lily is a further selected dwarf cultivar suitable for indoor or garden planting. However, there is a lack of relevant research on the cultivation adaptability of potted Asiatic lilies cultivars in the Chongqing region which in the southwest of China. This study selected five potted Asiatic lily cultivars, and the phenological period, stem and leaf characteristics, and flowering traits were assessed through statistical observation. The Asiatic lily ‘Tiny Ghost’ and ‘Tiny Double You’ are well-suited for both spring and autumn planting in Chongqing, while ‘Sugar Love’ and ‘Curitiba’ are best planted in the spring. The ‘Tiny Diamond’ is more appropriate for autumn planting due to its low tolerance to high temperature. The application of exogenous substances, including calcium chloride (CaCl₂), potassium fulvic acid (PFA) and melatonin (MT), can mitigate the detrimental effects of high-temperature stress on ‘Tiny Diamond’ by regulating photosynthesis, antioxidant systems, and osmotic substance content. A comprehensive evaluation using the membership function showed that the effect of exogenous CaCl₂ treatment is the best, followed by exogenous PFA treatment. CaCl₂ acts as a positive regulator of heat stress tolerance in Asian lilies, with potential applications in Asian lily cultivation. This study provides reference for cultivation and application of Asian lily varieties in Chongqing region, and also laid the foundation for further research on the mechanism of exogenous substances alleviating heat stress in lilies. Full article

This study introduces an innovative method for detecting risks in transmission line insulators by developing an optimized variant of YOLOv5, named Insulator-YOLO. The model addresses key challenges in small-defect detection, complex backgrounds, and computational efficiency. By incorporating GhostNetV2 in the backbone to streamline feature extraction and introducing SE and CBAM attention mechanisms, the model enhances its focus on critical features. The Bibi-directional Feature feature Pyramid pyramid Network network (BiFPN) is applied to enhance multi-scale feature fusion, and the integration of CIoU and NWD loss functions optimizes bounding box regression, achieving higher accuracy. Additionally, focal loss mitigates the imbalance between positive and negative samples, leading to more accurate and robust defect detection. Extensive evaluations demonstrate that Insulator-YOLO significantly improves detection accuracy and efficiency in real-world power line insulator defects, providing a reliable solution for maintaining the integrity of transmission systems. Full article

In order to solve the problems that existing tomato maturity detection methods struggle to take into account both common tomato and cherry tomato varieties in complex field environments (such as light change, occlusion, and fruit overlap) and the model size being too large, this paper proposes a lightweight tomato maturity detection model based on improved YOLO11, named GFS-YOLO11. In order to achieve a lightweight network, we propose the C3k2_Ghost module to replace the C3K2 module in the original network, which can ensure a feature extraction capability and reduce model computation. In order to compensate for the potential feature loss caused by the light weight, this paper proposes a feature-refining module (FRM). After embedding each feature extraction module in the trunk network, it improves the feature expression ability of common tomato and cherry tomato in complex field environments by means of depth-separable convolution, multi-scale pooling, and channel attention and spatial attention mechanisms. In addition, in order to further improve the detection ability of the model for tomatoes of different sizes, the SPPFELAN module is also proposed in this paper. In combining the advantages of SPPF and ELAN, multiple parallel SPPF branches are used to extract features of different levels and perform splicing and fusion. To verify the validity of the method, this study constructed a dataset of 1061 images of common and cherry tomatoes, covering tomatoes in six ripened categories. The experimental results show that the performance of the GFS-YOLO11 model is significantly improved compared with the original model; the P, R, mAP50, and MAP50-95 increased by 5.8%, 4.9%, 6.2%, and 5.5%, respectively, and the number of parameters and calculation amount were reduced by 35.9% and 22.5%, respectively. The GFS-YOLO11 model is lightweight while maintaining high precision, can effectively cope with complex field environments, and more conveniently meet the needs of real-time maturity detection of common tomatoes and cherry tomatoes. Full article

In the realm of construction site monitoring, ensuring the proper use of safety helmets is crucial. Addressing the issues of high parameter values and sluggish detection speed in current safety helmet detection algorithms, a feature-enhanced lightweight algorithm, LG-YOLOv8, was introduced. Firstly, we introduce C2f-GhostDynamicConv as a powerful tool. This module enhances feature extraction to represent safety helmet wearing features, aiming to improve the efficiency of computing resource utilization. Secondly, the Bi-directional Feature Pyramid (BiFPN) was employed to further enrich the feature information, integrating feature maps from various levels to achieve more comprehensive semantic information. Finally, to enhance the training speed of the model and achieve a more lightweight outcome, we introduce a novel lightweight asymmetric detection head (LADH-Head) to optimize the original YOLOv8-n’s detection head. Evaluations on the SWHD dataset confirm the effectiveness of the LG-YOLOv8 algorithm. Compared to the original YOLOv8-n algorithm, our approach achieves a mean Average Precision (mAP) of 94.1%, a 59.8% reduction in parameters, a 54.3% decrease in FLOPs, a 44.2% increase in FPS, and a 2.7 MB compression of the model size. Therefore, LG-YOLOv8 has high accuracy and fast detection speed for safety helmet detection, which realizes real-time accurate detection of safety helmets and an ideal lightweight effect. Full article

The use of a rapid heating method to achieve heterogeneity of Mn in medium-manganese steel and improve its comprehensive performance has been widely studied and these techniques have been widely applied. However, the heating rate (from α to γ) has not received sufficient attention with respect to its microstructure-evolution mechanism. In this study, the effect of heating rate on the microstructure evolution and hardness of heterogeneous medium-manganese steel was investigated by using X-ray diffraction (XRD), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), transmission electron microscopy (TEM) and DICTRA simulation. The results showed that the Mn distribution was heterogeneous in the initial microstructure of pearlite due to strong partitioning of Mn between ferrite and cementite. At low heating rates (<10 °C/s), the heterogeneity of Mn distribution was diminished to some extent due to the long-distance diffusion of Mn in high-temperature austenite. Contrastingly, at high heating rates, the initial heterogeneity of the Mn element could be largely preserved due to insufficient diffusion of Mn, which resulted in more ghost pearlite (GP: pearlite-like microstructure with film martensite/RA). Moreover, the high heating rate not only refines the prior austenite grain but also increases the total RA content, which is mainly composed of additional film RA. As the heating rate increases, the hardness gradually increases from 628.1 HV to 663.3 HV, due to grain refinement and increased dislocation density. Dynamic simulations have also demonstrated a strong correlation between this interesting microstructure and the non-equilibrium diffusion of Mn. Full article

The quality inspection of potato seed tubers is pivotal for their effective segregation and a critical step in the cultivation process of potatoes. Given the dearth of research on intelligent tuber-cutting machinery in China, particularly concerning the identification of bud eyes and defect detection, this study has developed a multi-target recognition approach for potato seed tubers utilizing deep learning techniques. By refining the YOLOv5s algorithm, a novel, lightweight model termed DCS-YOLOv5s has been introduced for the simultaneous identification of tuber buds and defects. This study initiates with data augmentation of the seed tuber images obtained via the image acquisition system, employing strategies such as translation, noise injection, luminance modulation, cropping, mirroring, and the Cutout technique to amplify the dataset and fortify the model’s resilience. Subsequently, the original YOLOv5s model undergoes a series of enhancements, including the substitution of the conventional convolutional modules in the backbone network with the depth-wise separable convolution DP_Conv module to curtail the model’s parameter count and computational load; the replacement of the original C3 module’s Bottleneck with the GhostBottleneck to render the model more compact; and the integration of the SimAM attention mechanism module to augment the model’s proficiency in capturing features of potato tuber buds and defects, culminating in the DCS-YOLOv5s lightweight model. The research findings indicate that the DCS-YOLOv5s model outperforms the YOLOv5s model in detection precision and velocity, exhibiting superior detection efficacy and model compactness. The model’s detection metrics, including Precision, Recall, and mean Average Precision at Intersection over Union thresholds of 0.5 (mAP1) and 0.75 (mAP2), have improved to 95.8%, 93.2%, 97.1%, and 66.2%, respectively, signifying increments of 4.2%, 5.7%, 5.4%, and 9.8%. The detection velocity has also been augmented by 12.07%, achieving a rate of 65 FPS. The DCS-YOLOv5s target detection model, by attaining model compactness, has substantially heightened the detection precision, presenting a beneficial reference for dynamic sample target detection in the context of potato-cutting machinery. Full article

In a recent contribution, we identified possible points of contact between the asymptotically safe and canonical approaches to quantum gravity. The idea is to start from the reduced phase space (often called relational) formulation of canonical quantum gravity, which provides a reduced (or physical) Hamiltonian for the true (observable) degrees of freedom. The resulting reduced phase space is then canonically quantized, and one can construct the generating functional of time-ordered Wightman (i.e., Feynman) or Schwinger distributions, respectively, from the corresponding time-translation unitary group or contraction semigroup, respectively, as a path integral. For the unitary choice, that path integral can be rewritten in terms of the Lorentzian Einstein–Hilbert action plus observable matter action and a ghost action. The ghost action depends on the Hilbert space representation chosen for the canonical quantization and a reduction term that encodes the reduction of the full phase space to the phase space of observables. This path integral can then be treated with the methods of asymptotically safe quantum gravity in its Lorentzian version. We also exemplified the procedure using a concrete, minimalistic example, namely Einstein–Klein–Gordon theory, with as many neutral and massless scalar fields as there are spacetime dimensions. However, no explicit calculations were performed. In this paper, we fill in the missing steps. Particular care is needed due to the necessary switch to Lorentzian signature, which has a strong impact on the convergence of “heat” kernel time integrals in the heat kernel expansion of the trace involved in the Wetterich equation and which requires different cut-off functions than in the Euclidian version. As usual we truncate at relatively low order and derive and solve the resulting flow equations in that approximation. Full article