small target detection
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Author(s):  
Qingyu Hou ◽  
Liuwei Zhang ◽  
Fanjiao Tan ◽  
Yuyang Xi ◽  
Haoliang Zheng ◽  
...  

2022 ◽  
Vol 15 (0) ◽  
pp. 1-9
Author(s):  
ZHAO Peng-peng ◽  
◽  
◽  
LI Shu-zhong ◽  
LI Xun ◽  
...  

2021 ◽  
Author(s):  
Dan Su ◽  
Qiong-lan Na ◽  
Hui-min He ◽  
Yi-xi Yang

Recently developed methods such as DETR [1] apply Transformer [2] structure to target detection. The performance of using Transformers for target detection (DETR) is similar to that of two-stage target detector. First of all, this paper attempts to apply Transformer to computer room personnel detection. The contributions of the improved DETR include: 1) in order to improve the poor performance of small target detection. Embed Depthwise Convolution in the encoder. When the coding feature is reconstructed, the channel information is retained. 2) in order to solve the problem of slow convergence in DETR training. This paper improves the cross-attention in DECODE and adds the spatial query module. It can accelerate the convergence of DETR. The convergence speed of the improved method is six times faster than that of the original DETR, and the mAP0.5 is improved by 3.1%.


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