The manufacturing industry is currently in a pivotal phase, experiencing a transformative convergence of IoT armed with Image Processing technologies. This combination is driving a future characterized by unparalleled efficiency, stringent quality control, and commitment to sustainability.
Unpacking the Power Duo: IoT and Image Processing
Envision the Internet of Things (IoT) as a vast interconnected web. It operates like a digital nervous system, binding devices across the internet for efficient and seamless data exchange. Crucial to this network are machine vision systems, which include high-resolution cameras and other visual sensors. They serve as 'digital eyes,' diligently capturing images of products moving along a production line. Behind those 'digital eyes', image processing acts as the 'brain' of the operation. It takes the data from the visual sensors, interpreting the visual information to extract valuable insights—whether it's spotting product defects or recognizing alignment issues.
In this blend of technologies, the IoT's primary role is to enable high-speed data transfer. It swiftly moves data from the sensors to a central processor for real-time analysis. As soon as an image is captured, it is sent via the IoT network for instantaneous processing. By continuously taking and analyzing data from the production line, an IoT based system armed with machine vision can immediately trigger an alert if a deviation from pre-set quality standards is detected; or even gradually build up a valuable database that in turn can feed multiple AI applications to improve quality standards.
The quest for efficiency is a central theme in the manufacturing industry, and the seamless incorporation of image processing into IoT systems is a key player in this narrative. An interesting study by Mital et al. provides a vivid illustration of this . They experimented with manual and hybrid inspection methods. The hybrid approach combined human intuition with computer-aided tools powered by IoT. The results were striking. The combination of human insight and machine precision, enhanced by the capabilities of IoT and image processing, led to a significant 60% reduction in inspection time and a 25% increase in defect detection efficiency.
In a deeper exploration into full automation, Zhou et al.'s study stands out . They devised a system that leveraged IoT technologies for the automatic inspection of surface defects in automobile parts. With the aid of image processing, the system could identify defects accurately, which resulted in an impressive 50% reduction in inspection time. This showcases the substantial efficiency gains that can be reaped when manufacturing processes are driven by the enhanced capabilities of IoT.
IoT technologies, particularly when paired with image processing, also have a transformative influence on waste reduction strategies in manufacturing. By introducing digital inspection systems, such as one discussed in the previous section, defects can be swiftly detected at the earliest stages of production, even in material selection and grading.
Bhatt and Pant's apple grading model offers an excellent case study . This innovative system utilized machine vision technology, enhanced by IoT, to capture high-resolution images of individual apples. With the help of image processing, features such as color, size, and skin quality were analyzed against quality benchmarks. The system could differentiate between supermarket-grade apples and those suited for juicing or cider production with an accuracy of 94.3%. This efficient use of apple harvests substantially reduced food waste, by effectively moving high grade apples to market shelves, and lower grades into different production lines like juice or cider making, clearly demonstrating the impact of IoT technologies on sustainable manufacturing practices.
Quality Control with AI: A Trio of Advancement
The evolution of quality control in manufacturing is remarkable, with a notable influence from the convergence of IoT, image processing, and the pivotal player—Artificial Intelligence (AI).
AI, with deep learning in particular, functions as the navigator, directing these technologies to ensure strict adherence to top-tier quality standards. The following remarkable studies will demonstrate how effective this trio can be in optimizing quality control. In a study, Fu et al.  have harnessed the power of high-resolution imaging and IoT-backed data transfering to design a system that detects and classifies defects on steel surfaces. Leveraging deep learning techniques, the system could identify a wide range of defect types, from simple surface scratches to complex distortions in the steel's structure. Their approach not only promptly identified defects but also categorized them based on their severity, with a formidable accuracy rate of 97.78%.
Similarly, Amin and Akhter  have developed a system that also uses IoT-enhanced image processing for defect identification on steel sheet surfaces. This system employs a sophisticated deep learning model, trained on thousands of steel surface images. As a result, the system has also shown stunning results, being able to discern minute defects that are potentially missed by human inspection, with a notable accuracy rate of 96.2%. These instances elucidate the potential of an AI-backed, IoT-integrated image processing system in enabling manufacturers to promptly detect, classify, and rectify defects, while also adjusting quality thresholds over time, ultimately setting new quality standards across the manufacturing industry. Although both of the studies remain in the research phase, they have opened up an opportunity to apply these solutions in real-life production.
Bring the Solution to Life: What to Consider
Harnessing IoT and image processing in manufacturing has been shown to offer multiple advantages, transforming processes, enhancing efficiency, and enabling unparalleled quality control. These technologies are versatile in their applications, significantly contributing to a revolution in the industry. However, their implementation is not a straightforward task. The inherent complexity of these systems, the regular calibration they require, and the substantial amounts of data they produce, all pose challenges that manufacturers need to address effectively.
With such intricacy involved, the question arises: who should manufacturers entrust with the set up, maintenance, and data handling tasks inherent to these technologies? What qualifications and expertise should this entity possess to proficiently manage the complexities and successfully leverage the enormous potential that IoT and image processing offer? The answers to these questions are key to unlocking the full potential of this digital transformation, driving the manufacturing industry toward a future of efficiency and productivity.
 A. Mital, M. Govindaraju, and B. Subramani, "A comparison between manual and hybrid methods in parts inspection," Integrated Manufacturing Systems, vol. 9, no. 6, pp. 344-349, 1998.
 Q. Zhou et al., "An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods," Sensors, vol. 19, no. 3, p. 644, 2019.
 A. K. Bhatt and D. Pant, "Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation," AI & SOCIETY, vol. 30, no. 1, pp. 45–56, 2013.
 G. Fu et al., "A deep-learning-based approach for fast and robust steel surface defects classification," Optics and Lasers in Engineering, vol. 121, pp. 397–405, 2019.
 D. Amin and S. Akhter, "Deep Learning-Based Defect Detection System in Steel Sheet Surfaces," 2020 IEEE Region 10 Symposium (TENSYMP), 2020.