Detecting the tiny defects of cigarette appearance based on a hybrid model of lightweight ViT and RCNN

Abstract

Detecting tiny defects in cigarettes is currently a major concern for manufacturers. To address this issue, this paper investigates a hybrid model based on lightweight ViT and RCNN to provide a better balance of high performance and high accuracy. Experiments showed that the model presented in this paper has a mAP value of 85.7% at 1% of tiny defects in cigarette appearance and an inference speed of 82 FPS in an acquisition scenario with a camera resolution of 1280×280, which meets the needs of high-speed acquisition in industrial sites. The results indicate that the hybrid model can be used to detect flaws in cigarette appearance.