IlluminationRobust Object Coordinate Detection by Adopting Pix2Pix GAN for Training Image Generation Illumination effects often result in error or failure in visual object localization. Whereas ConvNetbased object localization frameworks have shown tremendous robustness to the illumination effect, under some strong illumination effects, the system may still fail. In this paper, the authors propose a data augmentation method utilizing pix2pix GAN for automatic generation of object images under various illumination effects. Upon training for the object localization ConvNet, the generated images are included to enrich the training set for better performance under strong illumination effects. Experimental evidence shows that the accuracy of object coordinate detection can be improved significantly. The proposed framework maintains our concept of xe2x80x9coneshotxe2x80x9d where the user only needs to take a basis photo of the target object; the rest of the process including image processing and data annotation are all automated.

Low Illumination Enhancement For Object Detection In SelfDriving * Object detection plays an important role in the field of selfdriving. Illumination has a great impact on object detection, but most of the current methods do not solve the problem of object detection in poor light environment well. We propose a network that can optimize the image conversion which is based on the Cycle Generative Adversarial Networks (CycleGAN). We redesign the discriminator network of CycleGAN, add additional discriminators, optimize multiple parts of the network such as loss functions, and add object detection networks after converting the network. The Robot Car dataset of Oxford University is utilized to verify the effectiveness of the proposed method, and the results proved that our method can significantly improve the detection accuracy and increase the detected object number in low illumination environment.