Abstract
Extracting traffic information from images plays an increasingly significant role in Internet of vehicle. However, due to the high-speed movement and bumps of the vehicle, the image will be blurred during image acquisition. In addition, in rainy days, as a result of the rain attached to the lens, the target will be blocked by rain, and the image will be distorted. These problems have caused great obstacles for extracting key information from transportation images, which will affect the real-time judgment of vehicle control system on road conditions, and further cause decision-making errors of the system and even have a bearing on traffic accidents. In this paper, we propose a motion-blurred restoration and rain removal algorithm for IoV based on generative adversarial network and transfer learning. Dynamic scene deblurring and image de-raining are both among the challenging classical research directions in low-level vision tasks. For both tasks, firstly, instead of using ReLU in a conventional residual block, we designed a residual block containing three 256-channel convolutional layers, and we used the Leaky-ReLU activation function. Secondly, we used generative adversarial networks for the image deblurring task with our Resblocks, as well as the image de-raining task. Thirdly, experimental results on the synthetic blur dataset GOPRO and the real blur dataset RealBlur confirm the effectiveness of our model for image deblurring. Finally, as an image de-raining task based on transfer learning, we can fine-tune the pre-trained model with less training data and show good results on several datasets used for image rain removal.
Highlights
With the rapid development of automobile industry and the increasing number of vehicles, traffic safety and management problems have become more and more prominent
We found that using transfer learning, the model pretrained by a large number of image deblurring can achieve good results in image deraining tasks which is similar to image deblurring after fine-tuning with a small amount of image de-raining datasets
In contrast to the above mentioned methods of improving generative adversarial networks (GAN)’s shortcomings, we found that we can solve the shortcomings of GAN to some extent by a small trick, which we call the gradient training strategy
Summary
With the rapid development of automobile industry and the increasing number of vehicles, traffic safety and management problems have become more and more prominent. In order to improve the efficiency of road traffic, ensure the safety of drivers and vehicles and realize smart city and intelligent traffic, the interconnection between vehicles has become one of the key technologies. (IoV) technology has been proposed by researchers. In the IoV technology, the information of vehicles, roads and personnel can be collected by sensors such as radar and camera, which can realize real-time monitoring of road traffic conditions, detect vehicle and pedestrian information, and use communication technology to share information with other vehicles. As a considerable part of the Internet of Things, IoV can use multifarious communication technologies for data interconnection [1, 2]. Visual-based traffic information extraction has become one of the indispensable capabilities of vehicles. Vision-based information perception capabilities, such as pedestrian and vehicle detection, recognition and instance segmentation, require accurate feature learning of urban street scene images
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