Application of Computer Vision and Neural Networks in Feature Extraction and Optimization of Industrial Product Design

Computer-aided design (CAD) technology not only improves production efficiency but also optimizes design schemes through precise mathematical models. A neural network (NN) is a computational model that simulates human brain neurons with strong learning and optimization capabilities. It can automatically adjust network parameters based on input data and achieve various complex tasks. This article combines computer vision with NN, two advanced technologies, and applies them to the industrial design process. This method utilizes computer vision technology to extract features from CAD models, including information on shape, size, colour, texture, and other aspects. In industrial design, designers need to receive timely feedback and results in order to make adjustments and optimizations. This article's algorithm adopts more advanced calculation methods, reducing redundancy and complexity in the calculation process. Compared with traditional manual feature extraction methods, this method can automatically learn useful features from CAD models, avoiding the subjectivity of manual operations. By analyzing and integrating user needs, design solutions can be adjusted and optimized to meet their actual needs and expectations better. © 2024 U-turn Press LLC, http://www.cad-journal.net.