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The adverse haze weather condition has brought considerable difficulties in vision-based environmental applications. While, until now, most of the existing environmental monitoring studies are under ordinary conditions, and the studies of complex haze weather conditions have been ignored. Thence, this paper proposes a feature-supervised learning network based on generative adversarial networks (GAN) for environmental monitoring during hazy days. Its main idea is to train the model under the supervision of feature maps from the ground truth. Four key technical contributions are made in the paper. First, pairs of hazy and clean images are used as inputs to supervise the encoding process and obtain high-quality feature maps. Second, the basic GAN formulation is modified by introducing perception loss, style loss, and feature regularization loss to generate better results. Third, multi-scale images are applied as the input to enhance the performance of discriminator. Finally, a hazy remote sensing dataset is created for testing our dehazing method and environmental detection. Extensive experimental results show that the proposed method has achieved better performance than current state-of-the-art methods on both synthetic datasets and real-world remote sensing images.
Ke Wang; Siyuan Zhang; Junlan Chen; Fan Ren; Lei Xiao. A feature-supervised generative adversarial network for environmental monitoring during hazy days. Science of The Total Environment 2020, 748, 141445 .
AMA StyleKe Wang, Siyuan Zhang, Junlan Chen, Fan Ren, Lei Xiao. A feature-supervised generative adversarial network for environmental monitoring during hazy days. Science of The Total Environment. 2020; 748 ():141445.
Chicago/Turabian StyleKe Wang; Siyuan Zhang; Junlan Chen; Fan Ren; Lei Xiao. 2020. "A feature-supervised generative adversarial network for environmental monitoring during hazy days." Science of The Total Environment 748, no. : 141445.
China is the world's largest automotive market and is ambitious for autonomous vehicles (AVs) development. As one of the key goals of AVs, pedestrian safety is an important issue in China. Despite the rapid development of driverless technologies in recent years, there is a lack of researches on the adaptability of AVs to pedestrians. To fill the gap, this study would discuss the adaptability of current driverless technologies to China urban pedestrians by reviewing the latest researches. The paper firstly analyzed typical Chinese pedestrian behaviors and summarized the safety demands of pedestrians for AVs through articles and open database data, which are worked as the evaluation criteria. Then, corresponding driverless technologies are carefully reviewed. Finally, the adaptability would be given combining the above analyses. Our review found that autonomous vehicles have trouble in the occluded pedestrian environment and Chinese pedestrians do not accept AVs well. And more explorations should be conducted on standard human-machine interaction, interaction information overload avoidance, occluded pedestrians detection and nation-based receptivity research. The conclusions are very useful for motor corporations and driverless car researchers to place more attention on the complexity of the Chinese pedestrian environment, for transportation experts to protect pedestrian safety in the context of AVs, and for governors to think about making new pedestrians policies to welcome the upcoming driverless cars.
Ke Wang; Gang Li; Junlan Chen; Yan Long; Tao Chen; Long Chen; Qin Xia. The adaptability and challenges of autonomous vehicles to pedestrians in urban China. Accident Analysis & Prevention 2020, 145, 105692 .
AMA StyleKe Wang, Gang Li, Junlan Chen, Yan Long, Tao Chen, Long Chen, Qin Xia. The adaptability and challenges of autonomous vehicles to pedestrians in urban China. Accident Analysis & Prevention. 2020; 145 ():105692.
Chicago/Turabian StyleKe Wang; Gang Li; Junlan Chen; Yan Long; Tao Chen; Long Chen; Qin Xia. 2020. "The adaptability and challenges of autonomous vehicles to pedestrians in urban China." Accident Analysis & Prevention 145, no. : 105692.
We present a novel low-cost visual odometry method of estimating the ego-motion (self-motion) for ground vehicles by detecting the changes that motion induces on the images. Different from traditional localization methods that use differential global positioning system (GPS), precise inertial measurement unit (IMU) or 3D Lidar, the proposed method only leverage data from inexpensive visual sensors of forward and backward onboard cameras. Starting with the spatial-temporal synchronization, the scale factor of backward monocular visual odometry was estimated based on the MSE optimization method in a sliding window. Then, in trajectory estimation, an improved two-layers Kalman filter was proposed including orientation fusion and position fusion . Where, in the orientation fusion step, we utilized the trajectory error space represented by unit quaternion as the state of the filter. The resulting system enables high-accuracy, low-cost ego-pose estimation, along with providing robustness capability of handing camera module degradation by automatic reduce the confidence of failed sensor in the fusion pipeline. Therefore, it can operate in the presence of complex and highly dynamic motion such as enter-in-and-out tunnel entrance, texture-less, illumination change environments, bumpy road and even one of the cameras fails. The experiments carried out in this paper have proved that our algorithm can achieve the best performance on evaluation indexes of average in distance (AED), average in X direction (AEX), average in Y direction (AEY), and root mean square error (RMSE) compared to other state-of-the-art algorithms, which indicates that the output results of our approach is superior to other methods.
Ke Wang; Xin Huang; Junlan Chen; Chuan Cao; Zhoubing Xiong; Long Chen. Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost. Remote Sensing 2019, 11, 2139 .
AMA StyleKe Wang, Xin Huang, Junlan Chen, Chuan Cao, Zhoubing Xiong, Long Chen. Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost. Remote Sensing. 2019; 11 (18):2139.
Chicago/Turabian StyleKe Wang; Xin Huang; Junlan Chen; Chuan Cao; Zhoubing Xiong; Long Chen. 2019. "Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost." Remote Sensing 11, no. 18: 2139.