Few-Shot Waste Detection Based on Dual Attention and Dynamic Hard Sample Triplet Loss

Deep learning-based intelligent waste detection has been appealing and promising for resource conservation and environmental preservation. However, collecting and labeling numerous samples to train waste detection models is time-consuming and labor-intensive. This paper proposes a few-shot waste detection model based on a dual attention mechanism and Dynamic Hard Sample (DHS) triplet loss, named DHS-FSOD, which can effectively recognize and locate a new waste category object only with fine-tuning on a few annotated samples. First, the DHS-FSOD model used deformable convolution in the feature extraction network to improve the detection performance for the same category of objects with different morphology. Then, a dual attention module was designed to help the RPN network improve the quality of proposals and reduce the error classification rate by filtering out the feature information irrelevant to the object category in the query sample. Furthermore, the DHS triplet loss was proposed to improve the model's ability to distinguish wastes with similar appearances but different classes. The effectiveness of DHS-FSOD was verified by ablation and comparison experiments on the MS COCO and Huawei waste datasets.