CJS-YOLOv5n: A high-performance detection model for cigarette appearance defects

In tobacco production, cigarettes with appearance defects are inevitable and dramatically impact the quality of tobacco products. Currently, available methods do not balance the tension between detection accuracy and speed. To achieve accurate detection on a cigarette production line with the rate of 200 cigarettes per second, we propose a defect detection model for cigarette appearance based on YOLOv5n (You Only Look Once Version 5 Nano), called CJS-YOLOv5n (YOLOv5n with C2F (Cross Stage Partial (CSP) Bottleneck with 2 convolutions-fast), Jump Concat, and SCYLLA-IoU (SIoU)). This model incorporates the C2F module proposed in the state-of-the-art object detection network YOLOv8 (You Only Look Once Version 8). This module optimizes the network by parallelizing additional gradient flow branches, enhancing the model's feature extraction capability and obtaining richer gradient information. Furthermore, this model uses Jump Concat to preserve minor defect feature information during the fusion process in the feature fusion pyramid's P4 layer. Additionally, this model integrates the SIoU localization loss function to improve localization accuracy and detection precision. Experimental results demonstrate that our proposed CJS-YOLOv5n model achieves superior overall performance. It maintains a detection speed of over 500 FPS (frames per second) while increasing the recall rate by 2.3% and mAP (mean average precision)@0.5 by 1.7%. The proposed model is suitable for application in high-speed cigarette production lines

Optimized YOLOv7-tiny model for smoke detection in power transmission lines

Fire incidents near power transmission lines pose significant safety hazards to the regular operation of the power system. Therefore, achieving fast and accurate smoke detection around power transmission lines is crucial. Due to the complexity and variability of smoke scenarios, existing smoke detection models suffer from low detection accuracy and slow detection speed. This paper proposes an improved model for smoke detection in high-voltage power transmission lines based on the improved YOLOv7-tiny. First, we construct a dataset for smoke detection in high-voltage power transmission lines. Due to the limited number of real samples, we employ a particle system to randomly generate smoke and composite it into randomly selected real scenes, effectively expanding the dataset with high quality. Next, we introduce multiple parameter-free attention modules into the YOLOv7-tiny model and replace regular convolutions in the Neck of the model with Spd-Conv (Space-to-depth Conv) to improve detection accuracy and speed. Finally, we utilize the synthesized smoke dataset as the source domain for model transfer learning. We pre-train the improved model and fine-tune it on a dataset consisting of real scenarios. Experimental results demonstrate that the proposed improved YOLOv7-tiny model achieves a 2.61% increase in mean Average Precision (mAP) for smoke detection on power transmission lines compared to the original model. The precision is improved by 2.26%, and the recall is improved by 7.25%. Compared to other object detection models, the smoke detection proposed in this paper achieves high detection accuracy and speed. Our model also improved detection accuracy on the already publicly available wildfire smoke dataset Figlib (Fire Ignition Library)

Detection of cigarette appearance defects based on improved YOLOv4

Appearance defects are visible factors that affect the quality of cigarettes. Most of the consumer complaints received by tobacco companies are caused by appearance defects of cigarettes. Therefore, it is of great significance to reduce cigarettes with appearance defects. At present, tobacco factories mainly detect the appearance quality of cigarettes through manual sampling inspection. The manual method has low detection efficiency, it is difficult to unify the judgment standard, and it is easy to cause secondary pollution to cigarettes. According to the features of cigarette appearance defects, the YOLOv4 (You Only Look Once Version 4) model was improved for cigarette appearance defect detection. We have improved the following: 1) the channel attention mechanism was introduced into YOLOv4 to improve the detection precision; 2) the K-means++ algorithm was used to optimize the selection of clustering centers; 3) the spatial pyramid pooling (SPP) was replaced with atrous spatial pyramid pooling (ASPP) to improve the defect detection ability with different sizes; 4) the α-CIoU loss function was used to improve the detection precision. The mAP of our improved method reached 91.77%, the precision reached 93.32%, and the recall reached 88.81%. Compared with other models, our method has better comprehensive performance and better detection ability

Evolution of Nb–Ta Oxide Minerals and Their Relationship to the Magmatic-Hydrothermal Processes of the Nb–Ta Mineralized Syenitic Dikes in the Panxi Region, SW China

Previous geochemical and petrological studies have concluded that initially magmatic Nb–Ta mineralization is often modified by post-magmatic hydrothermal fluids; however, there is still a lack of mineralogical evidence for the syenite-related Nb–Ta deposit. From the perspective of Nb–Ta minerals, the pyrochlore supergroup minerals have significance for indicating the fluid evolution of alkaline rock or related carbonatite type Nb–Ta deposits. The Panzhihua–Xichang (Panxi) region is a famous polymetallic metallogenic belt in southwestern China, abound with a huge amount of Nb–Ta mineralized syenitic dikes. This study focuses on the mineral textures and chemical compositions of the main Nb–Ta oxide minerals (including columbite-(Fe), fersmite, fergusonite-(Y), and especially pyrochlore group minerals) in samples from the Baicao and Xiaoheiqing deposits, in the Huili area, Panxi region, to reveal the magma evolution process of syenitic-dike-related Nb–Ta deposits. The Nb–Ta oxides in the Huili syenites are commonly characterized by a specific two-stage texture on the crystal scale, exhibiting a complex metasomatic structure and compositional zoning. Four types of pyrochlore group minerals (pyrochlores I, II, III, and IV) formed in different stages were identified. The euhedral columbite-(Fe), fersmite, and pyrochlores I and II minerals formed in the magmatic fractional crystallization stage. Anhedral pyrochlore III minerals are linked to the activity of magma-derived hydrothermal fluids at the late stages of magma evolution. The pyrochlore IV minerals and fergusonite-(Y) tend to be more concentrated in areas that have undergone strong albitization, which is a typical phenomenon of hydrothermal alteration. These mineralogical phenomena provide strong evidences that the magmatic-hydrothermal transitional stage is the favored model for explaining the Nb–Ta mineralization process. It is also concluded that the changes in chemical composition and texture characteristics for pyrochlore group minerals could serve as a proxy for syenite-related Nb–Ta mineralization processes

Evolution of Nb–Ta Oxide Minerals and Their Relationship to the Magmatic-Hydrothermal Processes of the Nb–Ta Mineralized Syenitic Dikes in the Panxi Region, SW China

Previous geochemical and petrological studies have concluded that initially magmatic Nb–Ta mineralization is often modified by post-magmatic hydrothermal fluids; however, there is still a lack of mineralogical evidence for the syenite-related Nb–Ta deposit. From the perspective of Nb–Ta minerals, the pyrochlore supergroup minerals have significance for indicating the fluid evolution of alkaline rock or related carbonatite type Nb–Ta deposits. The Panzhihua–Xichang (Panxi) region is a famous polymetallic metallogenic belt in southwestern China, abound with a huge amount of Nb–Ta mineralized syenitic dikes. This study focuses on the mineral textures and chemical compositions of the main Nb–Ta oxide minerals (including columbite-(Fe), fersmite, fergusonite-(Y), and especially pyrochlore group minerals) in samples from the Baicao and Xiaoheiqing deposits, in the Huili area, Panxi region, to reveal the magma evolution process of syenitic-dike-related Nb–Ta deposits. The Nb–Ta oxides in the Huili syenites are commonly characterized by a specific two-stage texture on the crystal scale, exhibiting a complex metasomatic structure and compositional zoning. Four types of pyrochlore group minerals (pyrochlores I, II, III, and IV) formed in different stages were identified. The euhedral columbite-(Fe), fersmite, and pyrochlores I and II minerals formed in the magmatic fractional crystallization stage. Anhedral pyrochlore III minerals are linked to the activity of magma-derived hydrothermal fluids at the late stages of magma evolution. The pyrochlore IV minerals and fergusonite-(Y) tend to be more concentrated in areas that have undergone strong albitization, which is a typical phenomenon of hydrothermal alteration. These mineralogical phenomena provide strong evidences that the magmatic-hydrothermal transitional stage is the favored model for explaining the Nb–Ta mineralization process. It is also concluded that the changes in chemical composition and texture characteristics for pyrochlore group minerals could serve as a proxy for syenite-related Nb–Ta mineralization processes

Coarse-to-Fine Structure-Aware Artistic Style Transfer

Artistic style transfer aims to use a style image and a content image to synthesize a target image that retains the same artistic expression as the style image while preserving the basic content of the content image. Many recently proposed style transfer methods have a common problem; that is, they simply transfer the texture and color of the style image to the global structure of the content image. As a result, the content image has a local structure that is not similar to the local structure of the style image. In this paper, we present an effective method that can be used to transfer style patterns while fusing the local style structure to the local content structure. In our method, different levels of coarse stylized features are first reconstructed at low resolution using a coarse network, in which style color distribution is roughly transferred, and the content structure is combined with the style structure. Then, the reconstructed features and the content features are adopted to synthesize high-quality structure-aware stylized images with high resolution using a fine network with three structural selective fusion (SSF) modules. The effectiveness of our method is demonstrated through the generation of appealing high-quality stylization results and a comparison with some state-of-the-art style transfer methods

Self-Supervised Learning for Solar Radio Spectrum Classification

Solar radio observation is an important way to study the Sun. Solar radio bursts contain important information about solar activity. Therefore, real-time automatic detection and classification of solar radio bursts are of great value for subsequent solar physics research and space weather warnings. Traditional image classification methods based on deep learning often require considerable training data. To address insufficient solar radio spectrum images, transfer learning is generally used. However, the large difference between natural images and solar spectrum images has a large impact on the transfer learning effect. In this paper, we propose a self-supervised learning method for solar radio spectrum classification. Our method uses self-supervised training with a self-masking approach in natural language processing. Self-supervised learning is more conducive to learning the essential information about images compared with supervised methods, and it is more suitable for transfer learning. First, the method pre-trains using a large amount of other existing data. Then, the trained model is fine-tuned on the solar radio spectrum dataset. Experiments show that the method achieves a classification accuracy similar to that of convolutional neural networks and Transformer networks with supervised training

Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m

The safe operation of high-voltage transmission lines ensures the power grid’s security. Various foreign objects attached to the transmission lines, such as balloons, kites and nesting birds, can significantly affect the safe and stable operation of high-voltage transmission lines. With the advancement of computer vision technology, periodic automatic inspection of foreign objects is efficient and necessary. Existing detection methods have low accuracy because foreign objects attached to the transmission lines are complex, including occlusions, diverse object types, significant scale variations, and complex backgrounds. In response to the practical needs of the Yunnan Branch of China Southern Power Grid Co., Ltd., this paper proposes an improved YOLOv8m-based model for detecting foreign objects on transmission lines. Experiments are conducted on a dataset collected from Yunnan Power Grid. The proposed model enhances the original YOLOv8m by incorporating a Global Attention Module (GAM) into the backbone to focus on occluded foreign objects, replacing the SPPF module with the SPPCSPC module to augment the model’s multiscale feature extraction capability, and introducing the Focal-EIoU loss function to address the issue of high- and low-quality sample imbalances. These improvements accelerate model convergence and enhance detection accuracy. The experimental results demonstrate that our proposed model achieves a 2.7% increase in mAP_0.5, a 4% increase in mAP_0.5:0.95, and a 6% increase in recall