According to the latest data released by IDC, the Chinese new energy vehicle market exceeded 11 million units in 2024, representing a year-on-year increase of 38.1%. Among them, plug-in hybrid electric vehicles (PHEVs) (up 85.7% year-on-year growth) and range-extended electric vehicles (up 99.3% year-on-year growth) continued to increase their share in the new energy vehicle market, while the pure electric vehicle market also saw a year-on-year growth of 18.7%. The rise of electric and hybrid vehicles has brought new manufacturing demands. Compared with traditional gasoline vehicles, their powertrain structures are different, and the production and assembly precision requirements for key components such as batteries and motors are extremely high. For example, in the assembly of battery modules, any slight deviation can affect battery performance and safety. This necessitates more precise and efficient visual inspection methods. Machine vision software can monitor and analyze the dimensional measurement, defect detection, and assembly verification processes in real time during production, helping companies improve product quality and production efficiency.
Meanwhile, the development of autonomous vehicles has opened up vast application opportunities for machine vision software. Autonomous driving systems rely on various sensors to perceive their surroundings, and machine vision, as a crucial component, uses cameras to collect image information and employs advanced algorithms to process and analyze it, identifying various target objects such as roads, vehicles, pedestrians, and traffic signs, providing key information for autonomous driving decisions. As the level of autonomous driving continues to increase, higher challenges are placed on the performance, accuracy, and real-time capabilities of machine vision software, prompting continuous innovation and upgrades.
Many believe the main challenge in building vision systems lies in using hardware from different vendors. However, hardware isn't the problem; the real challenge lies in making software packages from different vendors work together seamlessly. Vision inspection teams must ensure they fully explore and understand the software underpinning machine vision systems. This is especially important for automotive OEMs and suppliers preparing for the surge in demand from electric, hybrid, and autonomous vehicles.
When procuring new machine vision solutions, enterprise users typically consider two key issues. First, the performance and suitability of the hardware; different inspection tasks require matching hardware such as cameras with appropriate resolution, frame rate, and field of view. Second, the functionality and ease of use of the software, including whether it possesses powerful image analysis algorithms, a user-friendly interface, and convenient programming interfaces. However, another question should not be overlooked: "Even if an enterprise needs to deploy vision hardware from different vendors, is there a way to run the entire system using a single vendor's unified vision software suite?"
In fact, the third question is more important and should be prioritized when deploying machine vision systems. This is because it helps quality inspection teams determine whether their upcoming investment will ultimately fix the problem or help avoid the most common pitfalls in traditional machine vision system design: dealing with a hodgepodge of software platforms from different vendors and the challenges of adding or updating hardware components. Software is the glue that holds these machine vision systems together; it determines whether different hardware components can work together in the way engineers need to achieve automated quality inspection. Whether trying to improve quality control or conducting compliance audits, software is key to connecting vision inspection technology with other business systems to support automated data acquisition and analysis. This interoperability is crucial because operations and IT teams need to continuously interact with the software to design inspection processes, train deep learning models, determine whether inspections can pass, meet service level agreements, and protect the brand's reputation for quality and safety.
Currently, there are user-friendly, scalable, and secure single-vendor software suites available on the market that can run various hardware components such as cameras, barcode scanners, sensors, and robotic arms for visual inspection, process automation, and traceability. These vision software suites can integrate cameras, sensors, and scanners from different vendors into a system without requiring operators to configure, learn, or manage different software packages, or pay licensing fees to multiple vendors. They can also interface with systems running robots to help guide robots in executing automated workflows.
Machine vision is not just a means to compensate for labor shortages or verify worker decisions; it is also a mechanism for building brand trust and protecting profit margins. Real-time analysis and data acquisition capabilities help engineering teams ensure that defective parts or vehicles are not missed by manual inspections and enable the team to take swift intervention when problems are detected.
In the past, many automotive OEMs and suppliers adopted machine vision to more easily meet stringent production tolerance requirements, ensure precise quality control, and keep up with evolving industry standards. This also meant that systems were built up gradually over time to meet increasingly complex production needs, ensure consistent inspection, and reduce defects. Without automation, it was difficult for companies to simultaneously ensure high throughput and regulatory compliance. However, in the pursuit of efficiency and repeatable accuracy, the automotive industry ultimately had to use multiple software packages, thus facing various related challenges.
As the automotive industry continues to evolve, machine vision software must also evolve accordingly. The automotive industry must be able to progressively adapt machine vision systems to changing business needs, as we are seeing with the growth of electric, hybrid, and autonomous vehicles. A unified software platform should not only handle current tasks but also support future technologies such as AI-driven inspection and deep learning models. This flexibility ensures that when electric and autonomous vehicles require more complex inspections, the software can be scaled up to meet these needs without requiring large-scale system overhauls.
Therefore, the next time we discuss automated inspection, we should consult with technology vendors or system integrators to determine if their recommended system design supports a single software suite operation, even if the hardware comes from different manufacturers. These issues should include interoperability, data security, solution scalability, 2D/3D inspection compatibility, current and future AI applications (including deep learning models), and required training time. Replacing hardware is straightforward, but suitable software allows future workflow updates to be minor tweaks rather than major overhauls. Engineering teams and programmers should focus on finding flexible and durable software that can run any hardware they need without leaving regrets months or years later.
In summary, emerging trends in the automotive industry have brought many new opportunities for the reshaping and development of machine vision software. Companies in the automotive industry should seize this opportunity, make reasonable selections of advanced machine vision software solutions, and promote their steady development in the fields of electric, hybrid, and autonomous vehicles.
