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Fast implementation of object detection algorithm based on homomorphic model transformation
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DOI：10.1016/j.neucom.2024.127313.png)
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摘要
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Recent years have seen significant advancements in computer vision, spearheaded by the advent of deep learning networks. However, the rigid architectures of these networks limit their potential. This study investigates how altering existing networks architecture can enhance performance. However, modifying layers in a model leads to a mismatch in pre-trained weights, resulting in the loss of a considerable amount of information embedded in the pre-trained model. Concurrently, an inefficient post -training process can lead to a significant waste of time if it does not fully leverage the pre-trained model weights. We propose a novel Homomorphic Model Transformation (HMT), based on functional algebraic identity, to overcome this challenge. HMT ensures that the model's output remains identical to that of the original model even after expanding its architecture. This enables post -training enhancements to commence from the highest point of the original model. Experiment results reveal that HMT significantly improve training efficiency, reducing post -training time by over 90%. Additionally, HMT significantly enhances model performance metrics in a short timeframe, demonstrating a 4.70% increase in AP on the AI -TOD dataset, a 6.88% improvement on the DOTA1.5 dataset, a 3.05% enhancement on the COCO2017 dataset, and a 4.89% elevation on the MRSAText dataset.
Keyword:
Object detection
Deep learning network
Homomorphic model transformation (HMT)
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6.5
论文数:
2.5W
被引数:
6.5W
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