Real-Time Foreign Object and Production Status Detection of Tobacco Cabinets Based on Deep Learning

Abstract

Visual inspection plays an important role in industrial production and can detect product defects at the production stage to avoid major economic losses. Most factories mainly rely on manual inspection, resulting in low inspection efficiency, high costs, and potential safety hazards. A real-time production status and foreign object detection framework for smoke cabinets based on deep learning is proposed in this paper. Firstly, the tobacco cabinet is tested for foreign objects based on the YOLOX, and if there is a foreign object, all production activities will be immediately stopped to avoid safety and quality problems. Secondly, the production status of tobacco cabinet is judged to determine whether it is in the feeding state by the YOLOX position locating method and canny threshold method. If it is not in the feeding state, then the three states of empty, full, and material status of the tobacco cabinet conveyor belt are judged based on the ResNet-18 image classification network. Ultilizing our proposed method, the accuracy of foreign object detection, feeding state detection and the conveyor belt of tobacco cabinet state detection are 99.13%, 96.36% and 95.30%, respectively. The overall detection time was less than 1 s. The experimental results show the effectiveness of our method. It has important practical significance for the safety, well-being and efficient production of cigarette factories.