YOLOv5 module architecture diagram.

<div><p>With the rapid development of industrial automation and intelligent manufacturing, defect detection of electronic products has become crucial in the production process. Traditional defect detection methods often face the problems of insufficient accuracy and inefficiency when dea...

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Bibliographic Details
Main Author:	Longjian Guo (22209036) (author)
Other Authors:	Jianming Meng (22209039) (author), Wei Hao (134390) (author), Deepak Kumar Jain (8171286) (author)
Published:	2025
Subjects:	Biochemistry Sociology Developmental Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified experimental results show pcb defect dataset multiple defect types task learning framework small object detection map @[ 0 grained defect segmentation fine segmentation capability defect detection deep learning segmentation tasks xlink "> tiny defects target detection reaching 0 rapid development production process particularly notable paper proposes overall performance mask r iou improving intelligent manufacturing industrial automation existing methods electronic products complex backgrounds become crucial accurate solution
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YOLOv5 module architecture diagram.

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