YOLOv5s general structure diagram.

<div><p>In the context of industrial automation, the accurate detection of small defects on bearing surfaces (dents, bruise, scratch) is crucial for the safe operation of equipment. However, traditional detection methods have problems such as insufficient feature extraction for small tar...

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Bibliographic Details
Main Author:	Haisong Xu (141729) (author)
Other Authors:	Xiaolin Shi (261143) (author), Han Zhang (110653) (author), Fan Yang (1413) (author), Yun Chen (279569) (author)
Published:	2025
Subjects:	Sociology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified reduces redundant computation realizes efficient computation normalized wasserstein distance mean average precision larger sensory field insufficient feature extraction homemade dataset indicate contextual information within bearing defect identification original loss function traditional detection methods mainstream detection models bearing surface defects small target detection former yolov5s model small defects bearing surfaces original nearest accurate detection small targets xlink "> safe operation module instead modified model model complication industrial automation experimental results effectively improves channel attention better aggregation background interference backbone network aware reassembly 7 %.
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YOLOv5s general structure diagram.

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