ArticleScientific reports2025
A lightweight infrared target detection network suitable for land and water surfaces.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Research on lung nodule detection in X-ray plain films based on improved YOLOv12 model.Scientific reports · 2026Article
- Learning to recover weak signals: A two-stage graph-based framework for all-weather RGB-T object detection.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The object detection network based on YOLO has been widely used in the fields of intelligent transportation and public safety. Compared with visible light target detection, infrared target detection can work normally even in low light or harsh environments. In the visible light scene, YOLOv7-tiny has the advantages of speed and accuracy. However, when YOLOv7-tiny is directly applied to the infrared scene, the model still has some shortcomings, such as weak ability to extract detailed features, serious loss of semantic information, and more computational resources. So, a lightweight infrared target detection network called LIWL-YOLO, which is suitable for detecting both water and land targets, is proposed in this paper. Firstly, the lightweight backbone called SPFNet is designed by integrating space-to-depth convolution (SPDConv) into the FasterNet, so as to improve the feature extraction ability and speed of YOLOv7-tiny for low resolution images. Secondly, the attention module called SAF-CA is designed and added to the neck layer to make the model pay more attention to the weak texture features in the image. Furthermore, in order to improve the extraction ability of the model for low contrast information in images, the exponential space pyramid pool module is designed to replace the SPPCSPC module in YOLOv7-tiny. Finally, the knowledge distillation method of MGD is used to compress the knowledge into the improved model with YOLOv7 as the teacher model, so as to further improve the accuracy of the model for infrared targets. This paper constructs a hybrid dataset named FLIR-WSL as the experimental dataset, which combines the FLIR-v2 dataset and infrared water surface target images collected by our team. The experimental results on FLIR-WSL mixed data sets show that the map value of LIWL-YOLO is 69%, which is 4.3% higher than that of YOLOv7-tiny, and the FPS value on RTX4060 graphics card is 93. LIWL-YOLO not only takes into account the detection ability of land and water targets in infrared scene, but also realizes the balance between accuracy and speed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.