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Deep-multiscale stratified aggregation
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AI
DOI：10.1007/s11760-025-04820-2.png)
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Abstract
En 中文
We propose a novel Deep-Multiscale Stratified Aggregation (D-MSA) module, which is specifically designed to enhance the extraction and fusion of multi-scale features across various receptive fields. In contrast to conventional convolutional architectures, D-MSA effectively bridges the gap between shallow and deep features by addressing differences in scale and semantic content. Integrated into the YOLO architecture, D-MSA significantly improves the model's capability to process complex multiscale information without compromising computational efficiency. Experiments demonstrate that the incorporation of D-MSA could lead to a notable improvement in the accuracy of object detection.
Keywords:
Deep-multiscale stratified aggregation
Receptive field
YOLO
Object detection
Journal
IF:
2.1
Papers:
877
Citations:
4.6K