Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
YOLO-IRS: Infrared Ship Detection Algorithm Based on Self-Attention Mechanism and KAN in Complex Marine Background
by
Wang, Yuwu
, Guo, Muran
, Zhou, Xiaohai
, Guo, Limin
in
Accuracy
/ Algorithms
/ Deep learning
/ Design
/ Feature extraction
/ Field of view
/ infrared image
/ Infrared imagery
/ Infrared imaging
/ Infrared imaging systems
/ KAN
/ Light
/ Liu E
/ Marine technology
/ Marine transportation
/ Navigation safety
/ Object recognition
/ Occlusion
/ Remote sensing
/ self-attention
/ Semantics
/ Sensors
/ Shipping industry
/ Signal to noise ratio
/ small object detection
/ Target detection
/ Tax administration and procedure
/ Taxation
/ Vision systems
2025
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
YOLO-IRS: Infrared Ship Detection Algorithm Based on Self-Attention Mechanism and KAN in Complex Marine Background
by
Wang, Yuwu
, Guo, Muran
, Zhou, Xiaohai
, Guo, Limin
in
Accuracy
/ Algorithms
/ Deep learning
/ Design
/ Feature extraction
/ Field of view
/ infrared image
/ Infrared imagery
/ Infrared imaging
/ Infrared imaging systems
/ KAN
/ Light
/ Liu E
/ Marine technology
/ Marine transportation
/ Navigation safety
/ Object recognition
/ Occlusion
/ Remote sensing
/ self-attention
/ Semantics
/ Sensors
/ Shipping industry
/ Signal to noise ratio
/ small object detection
/ Target detection
/ Tax administration and procedure
/ Taxation
/ Vision systems
2025
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
YOLO-IRS: Infrared Ship Detection Algorithm Based on Self-Attention Mechanism and KAN in Complex Marine Background
by
Wang, Yuwu
, Guo, Muran
, Zhou, Xiaohai
, Guo, Limin
in
Accuracy
/ Algorithms
/ Deep learning
/ Design
/ Feature extraction
/ Field of view
/ infrared image
/ Infrared imagery
/ Infrared imaging
/ Infrared imaging systems
/ KAN
/ Light
/ Liu E
/ Marine technology
/ Marine transportation
/ Navigation safety
/ Object recognition
/ Occlusion
/ Remote sensing
/ self-attention
/ Semantics
/ Sensors
/ Shipping industry
/ Signal to noise ratio
/ small object detection
/ Target detection
/ Tax administration and procedure
/ Taxation
/ Vision systems
2025
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
YOLO-IRS: Infrared Ship Detection Algorithm Based on Self-Attention Mechanism and KAN in Complex Marine Background
Journal Article
YOLO-IRS: Infrared Ship Detection Algorithm Based on Self-Attention Mechanism and KAN in Complex Marine Background
2025
Request Book From Autostore
and Choose the Collection Method
Overview
Infrared ship detection technology plays a crucial role in ensuring maritime transportation and navigation safety. However, infrared ship targets at sea exhibit characteristics such as multi-scale, arbitrary orientation, and dense arrangements, with imaging often influenced by complex sea–sky backgrounds. These factors pose significant challenges for the fast and accurate detection of infrared ships. In this paper, we propose a new infrared ship target detection algorithm, YOLO-IRS (YOLO for infrared ship target), based on YOLOv10, which improves detection accuracy while maintaining detection speed. The model introduces the following optimizations: First, to address the difficulty of detecting weak and small targets, the Swin Transformer is introduced to extract features from infrared ship images. By utilizing a shifted window multi-head self-attention mechanism, the window field of view is expanded, enhancing the model’s ability to focus on global features during feature extraction, thereby improving small target detection. Second, the C3KAN module is designed to improve detection accuracy while also addressing issues of false positives and missed detections in complex backgrounds and dense occlusion scenarios. Finally, extensive experiments were conducted on an infrared ship dataset: compared to the baseline model YOLOv10, YOLO-IRS improves precision by 1.3%, mAP50 by 0.5%, and mAP50–95 by 1.7%. Compared to mainstream detection algorithms, YOLO-IRS achieves higher detection accuracy while requiring relatively fewer computational resources, verifying the superiority of the proposed algorithm and enhancing the detection performance of infrared ship targets.
This website uses cookies to ensure you get the best experience on our website.