Prototypical contrastive learning based oriented detector for kitchen waste

Lihan Ouyang

doi:10.1016/j.neucom.2024.128239

Prototypical contrastive learning based oriented detector for kitchen waste

2024-10-01

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AI

DOI：10.1016/j.neucom.2024.128239

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Automatic detection of kitchen waste enables the identification and quantification of non-degradable materials, such as plastics, metals, and other substances that cannot be easily decomposed. This approach increases the efficiency of the waste disposal process with significant time and cost savings compared to manual sorting. However, detecting non-degradable waste automatically presents a great challenge due to the deformability and directional uncertainty of the objects in kitchen waste images. Object deformation leads to a lack of structural features, making it more difficult to distinguish objects. Direction uncertainty leads to redundant backgrounds when detecting with the horizontal box detector. To address these issues, we propose a kitchen waste detector (KWDet) for the automatic detection of non-degradable waste in kitchen waste. First, the KWDet introduces the oriented bounding box representation and rotated region of interest (RRoI) learner, which enables the oriented detection of non-degradable waste in kitchen waste. Second, the KWDet constructs a prototypical contrastive learning (PCL) based detection head to improve the classification performance by learning discriminative features of different non-degradable waste in kitchen waste. In the PCL based detection head, the prototype-sample contrastive loss is proposed to pull samples and prototypes from the same classes together while pushing samples and prototypes from the different classes apart. After that, we design the inter- prototype contrastive loss to learn the discriminative prototypes of different non-degradable waste. Finally, to evaluate the proposed KWDet, we construct a large kitchen waste detection dataset (KWDD), containing 13,873 kitchen waste images and 213,565 instances collected from Zhengzhou China with 5 classes. To validate the effectiveness of our proposed method, we performed ablation experiments and comparison experiments on the KWDD dataset. The experiments demonstrate that KWDet achieves a state-of-the-art (SOTA) mAP 50 of 71.5%, surpassing other object detection methods by more than 1.6% mAP 50 . Furthermore, our approach's Macro-F1 score outperforms the SOTA method by 4.0%. Extensive experiments on the KWDD demonstrate that the prototypical contrastive learning-based oriented detector is able to extract more robust features and better characterize the target location, suggesting that the KWDet is a powerful support for intelligent kitchen waste sorting.

Keyword:

Kitchen-waste detector

Prototypical contrastive learning

Oriented object detection

Convolutional neural network

Prototypical contrastive learning based oriented detector for kitchen waste

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