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.

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