Improved YOLOv8-CR Network for Detecting Defects of the Automotive MEMS Pressure Sensors

Micro-electro-mechanical system (MEMS) sensors have been widely used in the automotive field owing to their small size, low cost, and high reliability. Various defects are caused by complicated environments and multistep processes in the manufacturing process of automotive MEMS pressure sensors. These defects are often small in size but can significantly impact the performance of products. The traditional detection method is inefficient and inaccurate. In this study, an improved you only look once v8 (YOLOv8) C2f-RFCBAMConv (CR) network is proposed to detect the defects of the automotive MEMS pressure sensors. The network is an improved version of YOLOv8. The faster version of the CSP bottleneck with two convolutions (C2f) and receptive-field convolutional block attention (RFCBAMConv) modules are applied. The ablation experiments confirm the impact of C2f and RFCBAMConv modules on network performance. The improved YOLOv8-CR network can effectively enhance the accuracy and speed of defect detection of the automotive MEMS pressure sensor. Furthermore, comparisons are made between the improved YOLOv8-CR network and other networks such as YOLOv8-large, YOLOv5, and faster region-based convolutional neural network (Fast-RCNN), demonstrating its effectiveness in identifying five types of defects in the automotive MEMS pressure sensors. The improved YOLOv8-CR exhibits considerably superior defect detection capabilities with a mean average precision (mAP) of 94.98% and a single picture detection time of 42.68 ms on the test set. The improved YOLOv8-CR network is helpful in realizing real-time and accurate online monitoring of product quality in the key process of the automotive MEMS pressure sensors.