In the past few years, with the rapid development of computer execution capabilities, target recognition strategies based on convolutional neural networks have become mainstream algorithms in the field of object detection. However, due to the blurred background and dim light, the object detection task in the night environment still faces greater visual challenges. This article is strongly inspired by DCGAN (Deep Convolution Generative Adversarial Networks). We use night images as input, generate virtual target scenes similar to the daytime environment through game training of generators and discriminators; and to obtain high-precision detection results, we combine the currently very advanced Faster R-CNN (Regionbased Convolution Neural Networks) target detection system, through deep convolution feature fusion and multi-scale ROI (Region Of Interest) pooling. A series of experimental results show that our method achieves an mAP of 82.6% in the detection of its own night scene dataset, which is significantly higher than the original Faster R-CNN alone of 80.4%. Therefore, our method can meet the actual needs of target detection tasks in night scene. We sincerely hope that our approach will contribute to future research.

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