Abstract: To address the scarcity of samples for low-light object detection in certain environments, this paper proposed a low-light few-shot object detection method based on feature optimization. The method designed a denoising Wasserstein autoencoder(DNWAE)module and an adaptive variational feature aggregation(AVFA) module to address the problem of weak image feature information under low-light conditions and enhance important features. To reduce object classification confusion caused by limited training samples in few-shot learning, the paper designed a category information guided detection head(CIGDH) module to improve detection accuracy. Experimental results show that, compared with the selected mainstream few-shot object detection algorithms, this method achieves an average improvement of 9.3%–19.2% in detection accuracy after being trained on low-light datasets. Moreover, after being trained on normal-light datasets, this method achieves an average improvement of 3.0% in detection accuracy compared with the current state-of-the-art algorithm. The proposed algorithm is meaningful and has good application value for few-shot object detection under low-light conditions.

Key words: low-light environment, few-shot object detection, auto encoder, feature aggregation, classification confusion