Enhanced Detection of Syringe Defects Based on an Improved YOLOv7-Tiny Deep-Learning Model
Wenxuan Zhao,
Ling Wang,
Chentao Mao
et al.
Abstract:The timely and accurate identification of syringe defects plays a key role in effectively improving product quality in production lines of syringes. In this article, we collected a dataset of image samples representing five common types of syringe defects found on the production line. The dataset comprises over 5000 images, with an average of 3 different syringe defects per image. Based on this dataset, we designed a syringe defect detection model based on an improved YOLOv7-Tiny proposed in this paper. The mo… Show more
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