Real-Time Rain Detection and Wiper Control Employing Embedded Deep Learning

A state-of-the-art real-time rain detection and wiper control method is proposed in this article. Currently, commercial models adopt electronic sensors that can only sample the humidity of a small region of the windshield. The existing computer vision methods primarily focus on the detection and counting of raindrops and provide a recall rate of less than 70%. Here we adopted a holistic-view deep learning approach to build a visual classifier that is robust to large varieties of background scenes, illumination, and water forms. Specifically, Deep Residual Network (ResNet) was adopted as the visual classifier that distinguishes between rainy and fair street scenes and controls the wipers accordingly. To verify the practicality of the proposed deep learning framework, we tested the network on various embedded computing systems, including an embedded computing cluster. The results show that the deep learning rain detector outperforms previous state-of-the-art methods with higher rain recall and precision. It was also found that with the help of some graphic computation-enhancing components, commonly available embedded systems in the market can provide comparable performance to personal computers. While using the enhanced embedded systems to build a cluster, a performance superior to PC was witnessed. As the embedded system is cost-effective, small, and lighter, normalized performances for various aspects clearly show the competitive edge of the embedded systems and confirm the practicality of the proposed system. We also release the dataset of 160 k images used for training the visual rain detector.