Author:
Lou Haitong,Duan Xuehu,Guo Junmei,Liu Haiying,Gu Jason,Bi Lingyun,Chen Haonan
Abstract
Traditional camera sensors rely on human eyes for observation. However, the human eye 1
is prone to fatigue when observing targets of different sizes for a long time in complex scenes, and 2
human cognition is limited, which often leads to judgment errors and greatly reduces the efficiency. 3
Target recognition technology is an important technology to judge the target category in camera 4
sensor. In order to solve this problem, a small size target detection algorithm for special scenarios was 5
proposed by this paper. Its advantage is that this algorithm not only has higher precision for small 6
size target detection, but also can ensure that the detection accuracy of each size is not lower than the 7
existing algorithm. In this paper, a new down-sampling method was proposed, which could better 8
preserve the context feature information. The feature fusion network was improved to effectively 9
combine shallow information and deep information. A new network structure was proposed to 10
effectively improve the detection accuracy of the model. In terms of accuracy, it is better than: YOLOX, 11
YOLOXR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny and YOLOv8.Three authoritative public data sets 12
were used in this experiment: a) On Visdron data sets (small size targets), DC-YOLOv8 is 2.5% more 13
accurate than YOLOv8. b) On Tinyperson data sets (minimal size targets), DC-YOLOv8 is 1% more 14
accurate than YOLOv8. c) On PASCAL VOC2007 data sets (Normal size target), DC-YOLOv8 is 0.5% 15
more accurate than YOLOv8.
Cited by
12 articles.
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