Improving Quality Assurance: Automated Defect Detection in Soap Bar Packaging Using YOLO-V5
Abstract
In this study, a deep learning algorithm based on the You Only Look Once (YOLO-V5) approach is proposed for the visual inspection of soap bar packaging defects. The defects considered in this study include pack missing, pack opening, and label errors. Images capturing various packaging defects were collected using a hardware setup consisting of a USB camera and a laptop. The collected image dataset was processed using the annotation tool Labellmg. Subsequently, the defect detection model was trained using YOLO-V5 and tested on different images as well as on live camera feeds. An Arduino Mega connected to the laptop via serial communication (USB cable) was utilized to trigger an LED indicating the detection of a defect. The proposed research achieved an accuracy of 98% with an average processing speed of the trained model is 200ms per input image or frame.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A deep learning-based system for real-time packaging defect detection achieved 81.8% precision, 82.5% accuracy, and an mAP of 78.6%.
“Introduced a deep learning-based system for real-time packaging defect detection. The system achieved 81.8% precision, 82.5% accuracy, and an mAP of 78.6%.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [129]
Cites
Defect detection in printed circuit boards using you-only-look-once convolutional neural networks
Adibhatla, V. A., Chih, H.-C., Hsu, C.-C., Cheng, J., Abbod, M. F., & Shieh, J.-S. (2020) · Electronics · doi:10.3390/electronics9091547 · Cited by 179 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
- Authors
- Nishant, B., Gill, S. S., & Raj, B.
- Affiliations
- National Institute of Technical Teachers Training and Research, Chandigarh (IN)
- ECE NIT,Jalander,Punjab,India
- Venue
- 2023 International Conference on Electrical, Electronics, Communication and Computers (ELEXCOM)
- Publisher
- IEEE
- Published
- 2023-08-26
- Pages
- 1-6
- OpenAlex
- W4390486006
- Cited by
- 3
- References
- 1
- Citations by year
- 2025: 1 · 2024: 2
- FWCI
- 1.30
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Electrical, Electronics, Communication and Computers (ELEXCOM) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- automated defect detection, YOLOv5, visual inspection, quality assurance, object detection, label errors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
