Abstract
Recently the object detection algorithms have been widely used in various fields. In the highway monitoring scene, the performance of existing pedestrian detection algorithms degrade rapidly when the size of pedestrian decreased. To enhance the performance of detector, we designed a method named DSANet, which combines super-resolution with object detection algorithm, so that the detection network can capture more detailed features. Compared with the existing super-resolution algorithms, we integrate degeneration learning and self-attention module to make the super-resolution algorithm better fit the pedestrian detection. In particular, we introduce the MSAF module to fuse self attention information of different head numbers. The proposed super-resolution method provides better support for pedestrian detection. The experimental results show that the reconstructed SR image has richer detail features, which improves the accuracy of pedestrian detection.
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Wu, Y., Yu, H., Lv, Z., Yan, S. (2022). Super-Resolution Based on Degradation Learning and Self-attention for Small-Scale Pedestrian Detection. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_53
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