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| Object Detection Algorithm with Dual-Modal Rectification Fusion Based on Self-Guided Attention |
| ZHANG Jinglei1,2,3, GONG Wenhao1,2, JIA Xin3 |
1. Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems, Tianjin University of Technology, Tianjin 300384;
2. School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384;
3. Engineering Training Center, Tianjin University of Technology, Tianjin 300384 |
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Abstract The traditional dual-modal object detection algorithms struggle to overcome low-contrast noise in complex scenes, such as fog, glare and dark night, and they cannot recognize small-size objects effectively. To solve these problems, an object detection algorithm with dual-modal rectification fusion based on self-guided attention is proposed. Firstly, a dual-modal fusion network is designed to rectify the low-contrast noise in the input images(visible and infrared images) by channel and spatial feature rectification. Consequently, the complementary information is acquired from the rectified features to accurately achieve feature fusion and the detection accuracy of the algorithm in the complex scenes is improved. Secondly, a self-guided attention mechanism is established to learn the dependency among pixels in the images. Thus, the fusion capability of features at different scales and the detection accuracy of the algorithm for small-scale objects are improved. Extensive experiments on six datasets, including pedestrian datasets, pedestrian-vehicle datasets and aerial vehicle datasets, demonstrate the superiority of the proposed approach.
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Received: 21 July 2023
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| Fund:Youth Project of National Natural Science Foundation of China(No.62302335) |
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Corresponding Authors:
ZHANG Jinglei, Ph.D., professor. His research interests include pattern recognition and image proce-ssing.
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| About author:: GONG Wenhao, master student. His research interests include image processing and object detection.JIA Xin, Ph.D., lecturer. His research interests include machine learning, image processing and 3D reconstruction. |
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2023, Vol. 36 
