Real-time visual intelligence for defect detection in pharmaceutical packaging.

Autor: Vijayakumar A; School of Computing, SASTRA Deemed University, Thanjavur, 613401, India., Vairavasundaram S; School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India. vsubramaniyaswamy@gmail.com., Koilraj JAS; School of Computing, SASTRA Deemed University, Thanjavur, 613401, India., Rajappa M; School of Computing, SASTRA Deemed University, Thanjavur, 613401, India., Kotecha K; Symbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology, Symbiosis International University, Pune, 411045, India. head@scaai.siu.edu.in., Kulkarni A; School of Engineering, Swinburne University of Technology, Hawthorn, Australia.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Aug 13; Vol. 14 (1), pp. 18811. Date of Electronic Publication: 2024 Aug 13.
DOI: 10.1038/s41598-024-69701-z
Abstrakt: Defect detection in pharmaceutical blister packages is the most challenging task to get an accurate result in detecting defects that arise in tablets while manufacturing. Conventional defect detection methods include human intervention to check the quality of tablets within the blister packages, which is inefficient, time-consuming, and increases labor costs. To mitigate this issue, the YOLO family is primarily used in many industries for real-time defect detection in continuous production. To enhance the feature extraction capability and reduce the computational overhead in a real-time environment, the CBS-YOLOv8 is proposed by enhancing the YOLOv8 model. In the proposed CBS-YOLOv8, coordinate attention is introduced to improve the feature extraction capability by capturing the spatial and cross-channel information and also maintaining the long-range dependencies. The BiFPN (weighted bi-directional feature pyramid network) is also introduced in YOLOv8 to enhance the feature fusion at each convolution layer to avoid more precise information loss. The model's efficiency is enhanced through the implementation of SimSPPF (simple spatial pyramid pooling fast), which reduces computational demands and model complexity, resulting in improved speed. A custom dataset containing defective tablet images is used to train the proposed model. The performance of the CBS-YOLOv8 model is then evaluated by comparing it with various other models. Experimental results on the custom dataset reveal that the CBS-YOLOv8 model achieves a mAP of 97.4% and an inference speed of 79.25 FPS, outperforming other models. The proposed model is also evaluated on SESOVERA-ST saline bottle fill level monitoring dataset achieved the mAP50 of 99.3%. This demonstrates that CBS-YOLOv8 provides an optimized inspection process, enabling prompt detection and correction of defects, thus bolstering quality assurance practices in manufacturing settings.
(© 2024. The Author(s).)
Databáze: MEDLINE
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