Visual Detection Application of Lightweight Convolution and Deep Residual Networks in Wood Production

In order to improve the production capacity of traditional wood manufacturing industry, efficient wood quality and thickness detection is a challenging issue. This paper firstly carries out digital twin modeling for a drawer side panel processing line of a wood company and explores the efficiency problems existing in the links of quality inspection and thickness inspection of wood by means of value stream mapping. Therefore, we adopted a lightweight convolution neural network MobileNetV2 for wood quality detection, which realized efficient wood quality identification. In contrast, traditional convolution neural network has many weighting parameters and large scale of generating detection model, which makes it difficult to apply in situations with limited computing power and memory. Secondly, due to the stronger robustness and generalization ability of the residual network, we used ResNet to detect the wood thickness and obtain reliable performance. Finally, we reasonably embedded them in the whole wood production process and established the simulation model of production line before and after improvement in FlexSim simulation software. The experimental results show that the improved plan can simplify the workshop production process, increase the production balance rate by 29.07%, increase the product value-added rate from 0.08% to 0.11%, and shorten the production cycle by 2 hours. Performance indicators such as product inventory, number of people, and equipment utilization also improve significantly. Based on the above results, the validity of the production process improvement model proposed by US based on lightweight convolution and deep residual network is demonstrated.