In this paper, we propose in our novel generative framework the use of Generative Adversarial Networks (GANs) to generate features that provide robustness for object detection on reduced quality images. The proposed GAN-based Detection of Objects (GAN-DO) framework is not restricted to any particular architecture and can be generalized to several deep neural network (DNN) based architectures. The resulting deep neural network maintains the exact architecture as the selected baseline model without adding to the model parameter complexity or inference speed. We first evaluate the effect of image quality not only on the object classification but also on the object bounding box regression. We then test the models resulting from our proposed GAN-DO framework, using two state-of-the-art object detection architectures as the baseline models. We also evaluate the effect of the number of re-trained parameters in the generator of GAN-DO on the accuracy of the final trained model. Performance results provided using GAN-DO on object detection datasets establish an improved robustness to varying image quality and a higher mAP compared to the existing approaches.

本研究提出了一种基于生成对抗网络的目标检测框架（GAN-DO），可用于生成具有鲁棒性的特征以提高图像质量较差情况下的目标检测精度，不增加模型复杂度或推理速度，能通用于各种深度神经网络结构，并在常用数据集上实现了较高的检测准确性。

基于GAN的图像物体检测方法：针对不同质量图像的优化