| Autor: |
Zuhui Hu, Yaguang Jing, Guoqing Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
EURASIP Journal on Advances in Signal Processing, Vol 2023, Iss 1, Pp 1-13 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1687-6180 |
| DOI: |
10.1186/s13634-023-01002-5 |
| Popis: |
Abstract Aiming at the problem of poor effect of object detection with visible images under low light conditions, the decision-level fusion detection method of visible and infrared images is studied. Taking YOLOX as the object detection network based on deep learning, a decision-level fusion detection algorithm of visible and infrared images based on light sensing is proposed. Experiments are carried out on LLVIP dataset, which is a visible-infrared paired dataset for low light vision. Through comparative analysis, it is found that the decision-level fusion algorithm based on Soft-NMS and light sensing obtained the optimal AP value of 69.0%, which is 11.4% higher than the object detection with visible images and 1.1% higher than the object detection with infrared images. The experimental results show that the decision-level fusion algorithm based on Soft-NMS and light sensing can effectively fuse the complementary information of visible and infrared images, and improve the object detection effect under low light conditions. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
Full text from SpringerLink