An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles

The automatic classification of aluminum profile surface defects is of great significance in improving the surface quality of aluminum profiles in practical production. This classification is influenced by the small and unbalanced number of samples and lack of uniformity in the size and spatial distribution of aluminum profile surface defects. It is difficult to achieve high classification accuracy by directly using the current advanced classification algorithms. In this paper, digital image processing methods such as rotation, flipping, contrast, and luminance transformation were used to augment the number of samples and imitate the complex imaging environment in actual practice. A RepVGG with CBAM attention mechanism (RepVGG-CBAM) model was proposed and applied to classify ten types of aluminum profile surface defects. The classification accuracy reached 99.41%, in particular, the proposed method can perfectly classify six types of defects: concave line (cl), exposed bottom (eb), exposed corner bottom (ecb), mixed color (mc), non-conductivity (nc) and orange peel (op), with 100% precision, recall, and F1. Compared with the existing advanced classification algorithms VGG16, VGG19, ResNet34, ResNet50, ShuffleNet_v2, and basic RepVGG, our model is the best in terms of accuracy, macro precision, macro recall and macro F1, and the accuracy was improved by 4.85% over basic RepVGG. Finally, an ablation experiment proved that the classification ability was strongest when the CBAM attention mechanism was added following Stage 1 to Stage 4 of RepVGG. Overall, the method we proposed in this paper has a significant reference value for classifying aluminum profile surface defects

Classification of Typical Pests and Diseases of Rice Based on the ECA Attention Mechanism

Rice, a staple food crop worldwide, is pivotal in agricultural productivity and public health. Automatic classification of typical rice pests and diseases is crucial for optimizing rice yield and quality in practical production. However, infrequent occurrences of specific pests and diseases lead to uneven dataset samples and similar early-stage symptoms, posing challenges for effective identification methods. In this study, we employ four image enhancement techniques—flipping, modifying saturation, modifying contrast, and adding blur—to balance dataset samples throughout the classification process. Simultaneously, we enhance the basic RepVGG model by incorporating the ECA attention mechanism within the Block and after the Head, resulting in the proposal of a new classification model, RepVGG_ECA. The model successfully classifies six categories: five types of typical pests and diseases, along with healthy rice plants, achieving a classification accuracy of 97.06%, outperforming ResNet34, ResNeXt50, Shufflenet V2, and the basic RepVGG by 1.85%, 1.18%, 3.39%, and 1.09%, respectively. Furthermore, the ablation study demonstrates that optimal classification results are attained by integrating the ECA attention mechanism after the Head and within the Block of RepVGG. As a result, the classification method presented in this study provides a valuable reference for identifying typical rice pests and diseases

Design on the Winter Jujubes Harvesting and Sorting Device

According to the existing problems of winter jujube harvesting, such as the intensive labor of manual picking, damage to the surface of winter jujubes, a winter jujube harvesting and sorting device was developed. This device consisted of vibration mechanism, collection mechanism, and sorting mechanism. The eccentric vibration mechanism made the winter jujubes fall, and the umbrella collecting mechanism can collect winter jujube and avoid the impact of winter jujube on the ground, and the sorting mechanism removed jujube leaves and divided the jujube into two types, and the automatic leveling mechanism made the device run smoothly in the field. Through finite element analysis and BP (Back Propagation) neural network analysis, the results show that: The vibration displacement of jujube tree is related to the trunk diameter and vibration position; the impact force of winter jujubes falling is related to the elastic modulus of umbrella material; the collecting area can be increased four times for each additional step of the collection mechanism; jujube leaves can be effectively removed when blower wind speed reaches 45.64 m/s. According to the evaluation standard grades of the jujubes harvesting and sorting, the device has good effects and the excellent rate up to 90%, which has good practicability and economy

Metallothionein 1G functions as a tumor suppressor in thyroid cancer through modulating the PI3K/Akt signaling pathway

Abstract Background MT1G inactivation mediated by promoter methylation has been reported in thyroid cancer. However, the role of MT1G in thyroid carcinogenesis remains unclear. The aim of this study is to examine the biological functions and related molecular mechanisms of MT1G in thyroid cancer. Methods Methylation-specific PCR (MSP) was performed to analyze promoter methylation of MT1G and its relationship with clinicopathological characteristics of papillary thyroid cancer (PTC) patients. Conventional and real-time quantitative RT-PCR assays were used to evaluate mRNA expression. The functions of ectopic MT1G expression were determined by cell proliferation and colony formation, cell cycle and apoptosis, as well as cell migration and invasion assays. Results MT1G expression was frequently silenced or down-regulated in thyroid cancer cell lines, and was also significantly decreased in primary thyroid cancer tissues compared with non-malignant thyroid tissues. Promoter methylation, along with histone modification, contributes to MT1G inactivation in thyroid tumorigenesis. Moreover, our data showed that MT1G hypermethylation was significantly positively associated with lymph node metastasis in PTC patients. Importantly, restoring MT1G expression in thyroid cancer cells dramatically suppressed cell growth and invasiveness, and induced cell cycle arrest and apoptosis through inhibiting phosphorylation of Akt and Rb. Conclusions We have for the first time revealed that MT1G appears to be functional tumor suppressor involved in thyroid carcinogenesis mainly through modulating the phosphatidylinositol-3-kinase (PI3K)/Akt pathway and partially through regulating the activity of Rb/E2F pathway in this study. </jats:sec