Training with Noise Adversarial Network: A Generalization Method for Object Detection on Sonar Image

Qixiang Ma, Longyu Jiang, Wenxue Yu, Rui Jin, Zhixiang Wu, Fangjin Xu

Abstract: Object detection tasks for sonar image confront two major challenges, scarcity of dataset and perturbation of noise, which cause overfitting to models. The state-of-the-art object detection designed for optical images cannot address the issues because of the inherent differentiation between the optical image and sonar image. To tackle this problem, in this paper, we propose an adversarial training method to generalize the detector by introducing perturbation with specific noise property of sonar images during training stage. We design a sideway network which we name Noise Adversarial Network (NAN). The NAN is embedded into the state-of-the-art detector to generate adversarial examples which serve as assistant decision-making items to predict both class and bounding box, aiming to improve the generalization and noise robustness of the detector. To provide prior knowledge of noise perturbation to NAN, we also design a Noise Block (NB) for introducing noise in the upstream layers, which further improves noise robustness. Following the Faster R-CNN framework, the results of our experiments indicate a 8.9% mAP boost on our sonar image dataset. The detector equipped with NAN and NB also outperforms the baseline on noised test sets. Furthermore, it gains a 2.4% mAP boost on the optical image dataset PASCAL VOC 2007.