YOLO Network Optimization With a Single Circular Bounding Box for Detecting Defective Cigarettes

Abstract

The manufacturing industry utilizes computing technology, robot technology, artificial intelligence, and IoT to improve production processes and quality. In particular, object detection technology is used in various industrial fields, and object detection methods based on deep learning are attracting attention. The tobacco processing industry requires automated production facilities, and quality control for defects in product appearance is essential. Mainly because tobacco products are sold at high prices, poor appearance is a significant issue in terms of consumer complaints and processing costs. Therefore, accurate cigarette detection is essential. We propose a modified network structure based on the YOLOv4-Tiny network, and use it to build a network optimized for cigarette detection. The modified network uses a single circular bounding box for learning and fast detection. It utilizes visual techniques, such as gradient-weighted Class Activation Mapping (Grad-CAM) to analyze the degree of activation of the network to construct an optimal network. This reduces the size of the network and increases processing speed, while maintaining detection accuracy. This paper is expected to play an important role in quality control and efficient production in the manufacturing industry.