Dr. Yuanyao Lu Profile

Dr. Yuanyao Lu

at North China Univ of Technology

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Publications (4)

SPIE Journal Paper | 22 August 2024

RENA-Depth: toward recursion representation enhancement in neighborhood attention guided lightweight self-supervised monocular depth estimation

Chaochao Yang, Yuanyao Lu, Yongsheng Qiu, Yuantao Wang

OE, Vol. 63, Issue 08, 088103, (August 2024) https://doi.org/10.1117/12.10.1117/1.OE.63.8.088103

KEYWORDS: RGB color model, Performance modeling, Semantics, Data modeling, Covariance, Transformers, Optical engineering, Feature extraction, Education and training, Convolution

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SPIE Journal Paper | 10 November 2023

Small object detection for autonomous driving under hazy conditions on mountain motorways

Yuantao Wang, Yongsheng Qiu, Haiyang Jiang, Yuanyao Lu

OE, Vol. 62, Issue 11, 113101, (November 2023) https://doi.org/10.1117/12.10.1117/1.OE.62.11.113101

KEYWORDS: Object detection, Tunable filters, Autonomous driving, Education and training, Image processing, Image filtering, Optical engineering, Autonomous vehicles, Environmental sensing, Sensors

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SPIE Journal Paper | 10 November 2023

ARODNet: adaptive rain image enhancement object detection network for autonomous driving in adverse weather conditions

Yongsheng Qiu, Yuanyao Lu, Yuantao Wang, Haiyang Jiang

OE, Vol. 62, Issue 11, 118101, (November 2023) https://doi.org/10.1117/12.10.1117/1.OE.62.11.118101

KEYWORDS: Object detection, Rain, Image enhancement, Education and training, Fiber optic gyroscopes, Autonomous driving, Environmental sensing, Adverse weather, Detection and tracking algorithms, Image processing

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The current field of autonomous driving has achieved superior object detection performance in good weather conditions. However, the environment sensing capability of autonomous vehicles is severely affected in rainfall traffic environments. Although deep-learning-based image derain algorithms have made significant progress, integrating them with high-level vision tasks, such as object detection, remains challenging due to the significant differences between the derain and object detection algorithms. Additionally, the accuracy of object detection in real rain traffic environments is significantly reduced due to the domain transfer problem between the training dataset and the actual rain environment. To address this domain-shifting problem, we propose an adaptive rain image enhancement object detection network for autonomous driving in adverse weather conditions (ARODNet). This network architecture consists of an image adaptive enhancement module, an image derain module, and an object detection module. The baseline detection module (CBAM-YOLOv7) is built by incorporating the YOLOv7 object detection network into a feed-forward convolutional neural network, and it includes an attention module (CBAM). We propose a domain adaptive rain image enhancement module, DRIP, for low-quality images acquired under heavy rainfall conditions. DRIP enhances low-quality images on rainy days by adaptively learning multiple preprocessing weights. To remove the effects of rain patterns and fog clouds on image detection, we introduce DRIP-enhanced images into the depth estimation derain module (DeRain) to prevent rain and fog from obscuring the objects to be detected. Finally, the multistage joint training strategy is adopted to improve the training efficiency, and the object detection is performed while the image is derained. The efficacy of the ARODNet network for object detection in rainy weather traffic environments has been demonstrated through numerous quantitative and qualitative studies.

Proceedings Article | 21 August 2023 Paper

Global temporal pyramid for human abnormal action recognition

Shengnan Chen, Yuanyao Lu, Pengju Zhang, Yixian Fu

Proceedings Volume 12783, 127830A (2023) https://doi.org/10.1117/12.2692166

KEYWORDS: Action recognition, Video, Video surveillance, Convolution, Information visualization, Feature extraction, RGB color model, Visualization, Semantics, Network architectures

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