Water Puddle Detection Method for Road Scene Based on Self-Attention and Adversarial Learning

There has been much interest in the research of self-driving cars. Yet the detection of potentially dangerous obstacles on road makes this investigation more challenging. Water puddles, a typical obstacle of this kind, could trap a self-driving car or even cause serious accidents. Therefore, detecting water puddles is of great importance. To this end, this paper propose a novel water puddle detection model, URA-net(U shape Network with Attention for Road). Building its backbone on U-net(U shape Network) with residual and upsampling blocks added, URA-net combines both the reflection attention units and self-attention units, which can better characterize the dependence among image features so as to improve the representative capability to locate water puddles. Furthermore, a two-generator conditional adversarial network RWD-GAN (Redundant With Dual Generative Adversarial Network) is proposed, where two URA-Nets with a minor revision become the two generators to facilitate the information interaction in the adversarial learning process between the generators and the discriminator, as well as between the two generators themselves. Experiments on the public water puddle dataset demonstration that URA-net achieves 87.18% measure, while RWD-GAN can further improve the accuracy of URA-net, pushing F1-score to 88.54%. Both URA-net and RWD-GAN outperforms the state-of-the-arts. © 2023 Chinese Institute of Electronics. All rights reserved.