Abstract: To solve the problem of low accuracy of target detection under low illumination conditions at night， this paper proposes a generative adversarial network-based algorithm for high illumination visible light image generation. To improve the ability of the generator to extract features， a CBAM attention module is introduced in the converter module； To avoid the noise interference of artifacts in the generated images， the decoder of the generator is changed from the deconvolution method to the up-sampling method of nearest neighbour interpolation plus convolution layer； to improve the stability of the network training， the adversarial loss function is replaced from the cross-entropy function to the least-squares function. The generated visible images have the advantages of spectral information， rich detail information and good visibility enhancement compared with infrared images and night visible images， which can effectively obtain information about the target and scene. We verified the effectiveness of the method by image generation metrics and target detection metrics respectively， in which the mAP obtained from the test on the generated visible image improved by 11.7 percentage points and 30.2 percentage points respectively compared to the infrared image and the real visible image， which can effectively improve the detection accuracy and anti-interference capability of nighttime targets.

Key words: image generation, generative adversarial network, attention mechanism, object detection