Abstract
Weakly supervised detection performs significantly lower than the fully supervised methods due to the lack of detailed and precise annotations. Especially, its performance deteriorates more severely in low-light conditions with the lack of low-light datasets. To overcome these issues, we propose a new Low-Light Weakly Supervised Object Detection (LL-WSOD) framework. First, we propose a Progressive Low-light NoiseModule (PLNM) to train the model progressively with low light using the common datasets with normal light, greatly reducing the training difficulty. Next, a Residual Self-refinement Low-light Rebuild Module (ResSLRM) is proposed to allow convolutional neural networks to learn sharper features by rebuilding low-light features into normal-light images. Finally, a Pseudo Boundingbox Assisted Learning Module (PBALM) is designed to perform better low-light training using salient priors. The results show that the proposed LL-WSOD algorithm effectively detects objects under low-light conditions and achieves great results on the real low-light dataset ExDark.
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