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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 2: AAAI-22 Technical Tracks 2

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

February 1, 2023

Authors

Wenyu Liu

Zhejiang University Alibaba-Zhejiang University Joint Institute of Frontier Technologies


Gaofeng Ren

Alibaba Group


Runsheng Yu

The Hong Kong University of Science and Technology


Shi Guo

The Hong Kong Polytechnic University


Jianke Zhu

Zhejiang University Alibaba-Zhejiang University Joint Institute of Frontier Technologies


Lei Zhang

Alibaba Group Hong Kong Polytechnic University, Hong Kong, China


Proceedings:

No. 2: AAAI-22 Technical Tracks 2

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision II

Downloads:

Download PDF

Abstract:

Though deep learning-based object detection methods have achieved promising results on the conventional datasets, it is still challenging to locate objects from the low-quality images captured in adverse weather conditions. The existing methods either have difficulties in balancing the tasks of image enhancement and object detection, or often ignore the latent information beneficial for detection. To alleviate this problem, we propose a novel Image-Adaptive YOLO (IA-YOLO) framework, where each image can be adaptively enhanced for better detection performance. Specifically, a differentiable image processing (DIP) module is presented to take into account the adverse weather conditions for YOLO detector, whose parameters are predicted by a small convolutional neural network (CNN-PP). We learn CNN-PP and YOLOv3 jointly in an end-to-end fashion, which ensures that CNN-PP can learn an appropriate DIP to enhance the image for detection in a weakly supervised manner. Our proposed IA-YOLO approach can adaptively process images in both normal and adverse weather conditions. The experimental results are very encouraging, demonstrating the effectiveness of our proposed IA-YOLO method in both foggy and low-light scenarios. The source code can be found at https://github.com/wenyyu/Image-Adaptive-YOLO.

DOI:

10.1609/aaai.v36i2.20072


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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