Multiscale Progressive Fusion Filter Network for Infrared Small Target Detection

Zhang, Pengfei; Wang, Zhile; Bao, Guangzhen; Hu, Jianming; Shi, Tianjun; Sun, Guanjie; Gong, Jinnan

doi:10.1109/tgrs.2023.3343496

Cited by 2 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, their interpretability is dimer compared to that of traditional methods. Currently, some studies combine these two methods [ 34 , 50 , 51 ], inserting modules based on traditional methods into the network to complete certain sub-tasks. In the future, enhancing the deeper fusion of the two methods may lead to breakthroughs in this field.…”

Section: Summary and Future Outlooksmentioning

confidence: 99%

“…The method based on LCM primarily utilizes local information in the spatial domain [20], using the local contrast between image blocks and their neighborhoods as local features to construct saliency maps and segment small targets. Dai Zhang et al [50] proposed a multi-scale infrared small target detection method based on the combination of traditional methods and deep learning, achieving a good balance in background suppression and target extraction.…”

Section: Enhancing the Representation Capability Of Small Targetsmentioning

confidence: 99%

“…The architecture of the ALCL-Net they proposed is shown in Figure 2. Zhang et al [50] proposed a multi-scale infrared small target detection method based on the combination of traditional methods and deep learning, achieving a good balance in background suppression and target extraction.…”

Section: Enhancing the Representation Capability Of Small Targetsmentioning

confidence: 99%

See 2 more Smart Citations

Infrared Dim Small Target Detection Networks: A Review

Cheng,

Lai,

Xia

et al. 2024

Sensors

View full text Add to dashboard Cite

In recent years, with the rapid development of deep learning and its outstanding capabilities in target detection, innovative methods have been introduced for infrared dim small target detection. This review comprehensively summarizes public datasets, the latest networks, and evaluation metrics for infrared dim small target detection. This review mainly focuses on deep learning methods from the past three years and categorizes them based on the six key issues in this field: (1) enhancing the representation capability of small targets; (2) improving the accuracy of bounding box regression; (3) resolving the issue of target information loss in the deep network; (4) balancing missed detections and false alarms; (5) adapting for complex backgrounds; (6) lightweight design and deployment issues of the network. Additionally, this review summarizes twelve public datasets for infrared dim small targets and evaluation metrics used for detection and quantitatively compares the performance of the latest networks. Finally, this review provides insights into the future directions of this field. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the latest developments in infrared dim small target detection networks.

show abstract

Section: Summary and Future Outlooksmentioning

confidence: 99%

Section: Enhancing the Representation Capability Of Small Targetsmentioning

confidence: 99%