Pattern Feature Image Processing Method Based on Lightweight Neural Network Technology

In the context of artificial intelligence, machine vision technology has also become a research hotspot in the decoration industry. However, there are still many problems in the practical application process of engineering. Mainly affected by uncertain factors such as environmental noise, there is still no comprehensive method to determine the treatment plan for decoration pattern structure accurately. Neural network technology has been applied to various scenarios for recognition and has achieved good results. This paper provides a reference for balancing the accuracy and speed of the model by controlling the model parameters for sampling the characteristic structure of decoration patterns. Pre-train the pruned model using an embedded system and fully utilize a software-defined lightweight training model as a benchmark. Compared with traditional neural networks, it has the characteristics of a flexible structure, high computational efficiency, and strong adaptability. Moreover, based on the benchmark model for quantification, the experimental results of MobileNet and Adam quantization were compared. The feature recognition rate and computational cost were 8.4% and 11.3% lower than the comparison scheme, respectively, improving the efficiency and quality of image recognition. Optimization algorithms are more precise and have unique machine learning analysis capabilities, which can help enhance pattern recognition in the decoration industry and provide references for similar recognition needs in other industries.