How does a visual AI inspection system for automotive seats eliminate glare while preserving texture details using multi-angle lighting?
Publish Time: 2025-09-17
In modern automotive manufacturing, car seats, as a core component of the interior, directly impact brand image and user experience. High-end seats often use a combination of materials—high-gloss leather or PVC for the main support area, and matte fabric or suede for the side panels and trim. This enhances visual appeal while ensuring breathability and comfort. However, this combination of surfaces presents significant challenges for automated visual inspection: highly reflective areas are prone to glare and specular reflections, obscuring real defects; while matte areas have weak diffuse reflection, making it difficult to capture texture details. Traditional single-source lighting solutions cannot address both issues simultaneously. The automotive seat visual AI inspection system overcomes this by using multi-angle, coordinated lighting to achieve uniform imaging across all surfaces, suppressing glare while preserving the true texture of the material.The key to this lies in "light field design." Instead of relying on fixed-direction floodlights, the system uses a network of multiple, independently controllable light sources. These are positioned around and at different heights from the camera, including low-angle side lighting, coaxial light, dome-shaped diffuse light, strip-shaped oblique light, and polarized light. Each type has unique optical characteristics: low-angle light highlights subtle surface irregularities and scratches, enhancing fabric texture; coaxial light illuminates from the camera's perspective, minimizing specular reflection; and dome-shaped diffuse light softly illuminates the entire surface, eliminating shadows and hotspots, revealing the true color of the material.During inspection, the system activates a pre-set "lighting recipe" based on the seat model. For leather areas, the system automatically activates a polarized light combination. Polarized light, vibrating in a specific direction, combined with a polarizing filter on the camera, effectively filters out non-informative glare, preserving only the surface scattering light. This makes previously "washed-out" areas clear, revealing subtle creases, cracks, or uneven coatings. For matte fabrics, the system employs multi-directional oblique lighting, illuminating the fabric from various angles to highlight subtle shadows and enhance the contrast of the weave structure, revealing defects such as skipped threads, stains, or uneven density.Furthermore, the AI inspection system utilizes a "multi-frame fusion" strategy. During a single inspection cycle, the camera captures multiple images under different lighting conditions: one set for capturing the surface smoothness of leather, another for extracting fabric texture, and yet another for overall color consistency analysis. The AI algorithm then fuses these images at the pixel level, extracting the optimal information from each, generating a comprehensive quality image. This process, akin to layering multiple "visual perspectives," avoids information loss under single lighting conditions and prevents detail loss due to over- or underexposure.The dynamic response capability of the light source is also crucial. The system features a high-speed electronic shutter and a light source synchronization controller, ensuring precise timing between each flash and camera exposure. Even at high production line speeds, clear, artifact-free images are captured. Simultaneously, the light source brightness can be fine-tuned in real time to adapt to ambient light changes or the reflective properties of different colored seats—dark surfaces absorb more light, so the system automatically increases brightness; light surfaces reflect more light, so the output is appropriately reduced to prevent overexposure.The AI model in this process is not just a "judge," but also an "interpreter." Through deep learning, it understands the response characteristics of different materials under various lighting conditions, distinguishing real defects from lighting artifacts. For example, a "bright line" appearing under side lighting might be initially identified as a scratch, but if it shifts with the lighting angle, the system recognizes it as a reflection, not physical damage. This context-based intelligent judgment significantly reduces false positives.Ultimately, multi-angle lighting synergy is not merely an optimization of hardware layout, but a deep integration of "light and intelligence." It empowers the machine vision system with human-like adaptability—capable of discerning details on bright surfaces and textures on dark surfaces. When an inspection device can clearly display the subtle sheen of leather and the weave structure of fabric within the same field of view, accurately identifying micron-level defects, it is the result of the evolution of optical design, control algorithms, and artificial intelligence. During the silent inspection process, light is precisely controlled, details are faithfully reproduced, and quality is rigorously maintained.
