SSMDA: Self-Supervised Cherry Maturity Detection Algorithm Based on Multi-Feature Contrastive Learning

Rong-Li Gai (), Kai Wei and Peng-Fei Wang
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Rong-Li Gai: College of Information Engineering, Dalian University, Dalian 116622, China
Kai Wei: College of Information Engineering, Dalian University, Dalian 116622, China
Peng-Fei Wang: College of Information Engineering, Dalian University, Dalian 116622, China

Agriculture, 2023, vol. 13, issue 5, 1-13

Abstract: Due to the high cost of annotating dense fruit images, annotated target images are limited in some ripeness detection applications, which significantly restricts the generalization ability of small object detection networks in complex environments. To address this issue, this study proposes a self-supervised cherry ripeness detection algorithm based on multi-feature contrastive learning, consisting of a multi-feature contrastive self-supervised module and an object detection module. The self-supervised module enhances features of unlabeled fruit images through random contrastive augmentation, reducing interference from complex backgrounds. The object detection module establishes a connection with the self-supervised module and designs a shallow feature fusion network based on the input target scale to improve the detection performance of small-sample fruits. Finally, extensive experiments were conducted on a self-made cherry dataset. The proposed algorithm showed improved generalization ability compared to supervised baseline algorithms, with better accuracy in terms of mAP, particularly in detecting distant small cherries.

Keywords: contrastive learning; maturity detection; object detection; self-supervised learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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