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AI-Enhanced Industrial Sensors: From Adaptive Detection to Smart Manufacturing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: 25 April 2026 | Viewed by 1323

Special Issue Editor


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Guest Editor
Department of Electrical Engineering, I-Shou University, Kaohsiung 84001, Taiwan
Interests: artificial intelligence; signal processing; intelligent control; intelligent sensors; smart manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of artificial intelligence (AI), industries are moving beyond adaptive detection into the era of smart manufacturing. This Special Issue explores the future trajectory of AI in the industrial sector, examining how its applications can evolve from foundational tasks—such as defect detection and equipment monitoring—to comprehensive, intelligent process management and data-driven decision-making.

By integrating deep learning, big data analytics, and cloud computing, AI enhances product quality, reduces operational costs, accelerates development cycles, and strengthens industrial competitiveness. This issue highlights AI’s transformation from a standalone tool into a holistic solution spanning design, production, quality assurance, and after-sales service—ultimately enabling greater flexibility, efficiency, and customization in manufacturing.

Sensors play a pivotal role in this transition. As the primary data acquisition gateway, they capture real-time, high-precision data from production lines, equipment, and environments—including temperature, pressure, vibration, imagery, and sound. Without reliable sensing data, AI algorithms lack the foundation for accurate predictions or decisions.

Topics of interest include the following:

  • AI-enhanced defect detection and anomaly prediction;
  • Real-time sensor data fusion for adaptive process control;
  • Self-calibrating and energy-autonomous sensor systems;
  • Human–machine collaboration through intelligent sensing.

In summary, this Special Issue investigates how AI advances from intelligent detection to integrated smart manufacturing, emphasizing its impact on industrial efficiency, quality, and flexibility. It also underscores the critical synergy between AI and sensor technologies in driving innovation for the future of industry.

Prof. Dr. Rey-Chue Hwang
Guest Editor

Manuscript Submission Information

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Keywords

  • artificial intelligence (AI)
  • smart manufacturing
  • adaptive detection
  • deep learning
  • big data analytics
  • cloud computing
  • process optimization
  • quality assurance
  • decision support systems
  • sensors

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Published Papers (2 papers)

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Research

34 pages, 11986 KB  
Article
High-Speed Die Bond Quality Detection Using Lightweight Architecture DSGβSI-SECS-Yolov7-Tiny
by Bao Rong Chang, Hsiu-Fen Tsai and Wei-Shun Chang
Sensors 2025, 25(23), 7358; https://doi.org/10.3390/s25237358 - 3 Dec 2025
Viewed by 291
Abstract
The die bonding process significantly impacts the yield and quality of IC packaging, and its quality detection is also a critical image sensing technology. With the advancement of machine automation and increased operating speeds, the misclassification rate in die bond image inspection has [...] Read more.
The die bonding process significantly impacts the yield and quality of IC packaging, and its quality detection is also a critical image sensing technology. With the advancement of machine automation and increased operating speeds, the misclassification rate in die bond image inspection has also risen. Therefore, this study develops a high-speed intelligent vision inspection model that slightly improves classification accuracy and adapts to the operation of new-generation machines. Furthermore, by identifying the causes of die bonding defects, key process parameters can be adjusted in real time during production, thereby improving the yield of the die bonding process and substantially reducing manufacturing cost losses. Previously, we proposed a lightweight model named DSGβSI-YOLOv7-tiny, which integrates depthwise separable convolution, Ghost convolution, and a Sigmoid activation function with a learnable β parameter. This model enables real-time and efficient detection and prediction of die bond quality through image sensing. We further enhanced the previous model by incorporating an SE layer, ECA-Net, Coordinate Attention, and a Small Object Enhancer to accommodate the faster operation of new machines. This improvement resulted in a more lightweight architecture named DSGβSI-SECS-YOLOv7-tiny. Compared with the previous model, the proposed model achieves an increased inference speed of 294.1 FPS and a Precision of 99.1%. Full article
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21 pages, 2516 KB  
Article
Wide-Area Visual Monitoring System Based on NB-IoT
by Guohua Qiu, Weiyu Tao, Rey-Chue Hwang and Chaofan Xie
Sensors 2025, 25(21), 6589; https://doi.org/10.3390/s25216589 - 26 Oct 2025
Cited by 1 | Viewed by 834
Abstract
Effective detection of unexpected events in wide-area surveillance remains a critical challenge in the development of intelligent monitoring systems. Recent advancements in Narrowband Internet of Things (NB-IoT) and 5G technologies provide a robust foundation to address this issue. This study presents an integrated [...] Read more.
Effective detection of unexpected events in wide-area surveillance remains a critical challenge in the development of intelligent monitoring systems. Recent advancements in Narrowband Internet of Things (NB-IoT) and 5G technologies provide a robust foundation to address this issue. This study presents an integrated architecture for real-time event detection and response. The system utilizes the Constrained Application Protocol (CoAP) to transmit encapsulated JPEG images from NB-IoT modules to an Internet of Things (IoT) server. Upon receipt, images are decoded, processed, and archived in a centralized database. Subsequently, image data are transmitted to client applications via WebSocket, leveraging the Message Queuing Telemetry Transport (MQTT) protocol. By performing temporal image comparison, the system identifies abnormal events within the monitored area. Once an anomaly is detected, a visual alert is generated and presented through an interactive interface. The test results show that the image recognition accuracy is consistently above 98%. This approach enables intelligent, scalable, and responsive wide-area surveillance reliably, overcoming the constraints of conventional isolated and passive monitoring systems. Full article
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