An improved YOLACT algorithm for instance segmentation of stacking parts

Instance segmentation is a very important task for a variety of applications. Instance segmentation for stacking objects is a challenge for computer vision. To overcome the challenge, we propose an improved YOLACT (You Only Look At CoefficienTs) algorithm. To improve the accuracy of feature extracti...

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Vydáno v:Digital signal processing Ročník 161; s. 105145
Hlavní autoři: Chao, Yongsheng, Zhang, Xiaochen, Rong, Guolin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.06.2025
Témata:
AC-FPN
Instance segmentation
Mechanical parts
MLCA
Stacking scene
YOLACT
Instance segmentation
YOLACT
MLCA
AC-FPN
Stacking scene
Mechanical parts
ISSN:1051-2004
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Shrnutí:Instance segmentation is a very important task for a variety of applications. Instance segmentation for stacking objects is a challenge for computer vision. To overcome the challenge, we propose an improved YOLACT (You Only Look At CoefficienTs) algorithm. To improve the accuracy of feature extraction, detection and segmentation in a densely stacking scene, a Multi-Level Feature Fusion and Channel Attention Mechanism Module (MLCA) are integrated with YOLACT's backbone. Further, to expand the receptive field without compromising image quality, we substitute the conventional Feature Pyramid Network (FPN) with an Attention-guided Context Feature Pyramid Module (AC-FPN). The effectiveness of the improved YOLACT algorithm is validated through extensive experiments on a customized dataset of stacking mechanical parts. Results demonstrate that the improved YOLACT algorithm significantly surpasses the other algorithms in detection and segmentation without notably increasing computing time.
ISSN:1051-2004
DOI:10.1016/j.dsp.2025.105145