Tire Texture Monitoring (VGG 19 VS Efficient Net b7)

Saloni Jain1, Sunita GP2, Sampath Kumar S3
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Abstract

Tires are crucial components of vehicles, continuously in contact with the road. Monitoring tire conditions is vital for safety and performance, as degradation in tire treads and sidewalls can affect traction, fuel efficiency, longevity, and road noise. This research leverages both VGG19 and Efficient Net B7 algorithms to enhance tire image rendering, addressing limitations of traditional techniques. Using a binary classification algorithm, we classify tire images as healthy or cracked. By fine-tuning VGG19 and EfficientNet B7 on a specialized tire dataset, we achieve high-quality, photorealistic renderings. Our results demonstrate remarkable improvements in texture quality and visual realism compared to traditional methods. The rendered images exhibit finer details and more accurate representations of the tire’s tread patterns and material properties. This research contributes to the field of computer graphics by presenting a novel application of deep learning techniques to a specific industrial need, paving the way for future advancements in high-quality rendering of complex tire textures. Key Words: VGG19,Photorealistic rendering, deep learning.
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轮胎纹理监测(VGG 19 VS Efficient Net b7)
轮胎是车辆的关键部件,与路面持续接触。监测轮胎状况对安全和性能至关重要,因为轮胎胎面和胎侧的退化会影响牵引力、燃油效率、使用寿命和路面噪音。这项研究利用 VGG19 和 Efficient Net B7 算法来增强轮胎图像渲染,解决了传统技术的局限性。我们采用二元分类算法,将轮胎图像分为健康和破裂两种。通过在专门的轮胎数据集上对 VGG19 和 EfficientNet B7 进行微调,我们实现了高质量的逼真渲染。与传统方法相比,我们的结果表明在纹理质量和视觉逼真度方面有了明显改善。渲染后的图像细节更精细,对轮胎胎面花纹和材料属性的表现更准确。这项研究将深度学习技术新颖地应用于特定的工业需求,为未来高质量渲染复杂轮胎纹理铺平了道路,从而为计算机图形学领域做出了贡献。关键字VGG19、逼真渲染、深度学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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