PC-Yolo: enhanced YOLOv5-based defect detection system with improved partial convolution for ham sausage inspection

Hexue Cai; Haonan Shangguan; Lulin Wang

doi:10.1117/12.3037840

12 September 2024 PC-Yolo: enhanced YOLOv5-based defect detection system with improved partial convolution for ham sausage inspection

Hexue Cai, Haonan Shangguan, Lulin Wang

Author Affiliations +

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 132561T (2024) https://doi.org/10.1117/12.3037840
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

Advances in automation for the packaging industry necessitate robust quality control measures, particularly in food appearance inspection. This paper introduces PC-Yolo, an advanced defect detection system based on an enhanced version of the YOLOv5 algorithm, tailored for the inspection of ham sausage products. PC-Yolo incorporates Partial Convolution (PConv) to improve the defect detection process, offering substantial gains in speed and accuracy over traditional methods. A custom dataset comprising high-resolution images of ham sausages, with and without defects, was used to train and validate the model. The proposed system efficiently handles variations in defect size through optimal prior box dimensionality and employs a combination of Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) for effective feature fusion. The resultant PC-Yolo model demonstrates superior real-time detection capabilities, with robust performance in complex scenarios, thereby addressing the industry's zero-defect challenge.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hexue Cai, Haonan Shangguan, and Lulin Wang "PC-Yolo: enhanced YOLOv5-based defect detection system with improved partial convolution for ham sausage inspection", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 132561T (12 September 2024); https://doi.org/10.1117/12.3037840

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Object detection

Convolution

Defect detection

Education and training

Data modeling

Inspection

Quality control

Show All Keywords

Keywords/Phrases

Search In:

Publication Years