The accurate and efficient detection of printed circuit board (PCB) surface defects is crucial to the electronic information manufacturing industry. However, current approaches to PCB defect detection face challenges, including large model sizes and difficulties in balancing detection accuracy with speed. To address these challenges, this paper proposes a novel PCB surface defect detection algorithm, named DVCW-YOLO. First, all standard convolutions in the backbone and neck networks of YOLOv8n are replaced with lightweight DWConv convolutions. In addition, a self-designed C2fCBAM module is introduced to the backbone network for extracting features. Next, within the neck structure, the C2f module is substituted with the more lightweight VOVGSCSP module, thereby reducing model redundancy, simplifying model complexity, and enhancing detection speed. By enhancing prominent features and suppressing less important ones, this modification allows the model to better focus on key regions, thereby improving feature representation capabilities. Finally, the WIoU loss function is implemented to replace the traditional CIoU function in YOLOv8n. This adjustment addresses issues related to low generalization and poor detection performance for small objects or complex backgrounds, while also mitigating the impact of low-quality or extreme samples on model accuracy. Experimental results demonstrate that the DVCW-YOLO model achieves a mean average precision (mAP) of 99.3% and a detection speed of 43.3 frames per second (FPS), which represent improvements of 4% and 4.08%, respectively, over the YOLOv8n model. These results confirm that the proposed model meets the real-time PCB defect detection requirements of small and medium-sized enterprises.

This paper presents an enhanced YOLOX-based algorithm for pest detection, adopting a nature-inspired approach for refining its methodology. To tackle the limited availability of image data pertaining to pests and diseases, the paper incorporates Mosaic and Mixup technologies for effective image preprocessing. Furthermore, a novel training strategy is proposed to enhance the overall quality of the results. The existing architecture is enriched by integrating shallow information, while the CLT module is devised to facilitate cross-layer fusion and extract essential feature information. This advancement enables improved object detection across various scales. Additionally, the paper optimizes the original PFPN structure by eliminating the convolutional layer preceding upsampling, enhancing the C3 module, and integrating the convolutional attention model (CBAM) to identify salient regions within complex scenes. The performance of the proposed CLT-YOLOX model is extensively evaluated using the IP102 dataset, demonstrating its effectiveness. Notably, the model exhibits significant improvements compared to the original AP evaluation index, with an increase of 2.2% in average precision (mAP) and 1.8% in AP₇₅. Furthermore, favorable results are achieved in the COCOmAP index, particularly in the AP_small category where there is a 2.2% improvement in performance.

Under the auspice of further developing 5G mobile communication technology and integrating it with the latest advancements in the field of Industrial Internet-of-Things, this study conducts in-depth research and detailed analysis on the combination of 5G with industrial systems based on composite structures, communication network architectures, wireless application scenarios, and communication protocols. The status quo, development trend, and necessity of 5G mobile communication technology are explored and its potential in industrial applications is analyzed. Based on the current practical development level of 5G technology, by considering different requirements for bandwidth, real-time performance, and reliability in communication networks of industrial systems, this study proposes three feasible paths for the integration between 5G and industrial systems, including the method to use 5G in place of field buses. Finally, by introducing real-world cases, this study has successfully demonstrated the integration of 5G and industrial systems by extending 5G terminals as field bus gateways. This study provides valuable references for research and practice in related fields.

This paper proposes a rolling bearing fault diagnosis method based on Variational Mode Decomposition–Newton–Raphson (VMD-NR) modal decomposition and multi-feature fusion to address the issue of insufficient accuracy in rolling bearing fault diagnosis under complex noisy environments, which is caused by inadequate feature extraction and weak model representation capabilities. Firstly, based on the Variational Mode Decomposition (VMD) theory, the Newton–Raphson-Based Optimization (NRBO) algorithm is introduced to construct an improved VMD model, enhancing the accuracy and anti-interference capability of fault signal decomposition. Secondly, a Wide-Dilated Convolutional Neural Network (WDCNN)–Informer dual-branch fusion architecture is proposed to collaboratively extract local and global long-sequence features from vibration signals. Through feature-level concatenation and fusion, feature complementarity is achieved, improving the completeness and effectiveness of signal feature representation. Experimental results on the Case Western Reserve University (CWRU) public bearing dataset demonstrate that the proposed diagnostic method achieves a fault classification accuracy of 99.8%, an increase of 5% compared with conventional methods, enabling precise rolling bearing fault classification and providing a theoretical basis for rolling bearing fault diagnosis.