What question did this study set out to answer?

This research aims to improve the accuracy and robustness of infrared small target detection through network optimization.

June 8, 2026Open Access

IR-WSANet: A lightweight network with frequency-spatial joint optimization for infrared small target detection

Key Points

This research aims to improve the accuracy and robustness of infrared small target detection through network optimization.
Developed IR-WSANet based on YOLOv10 with discrete wavelet transform convolution to enhance feature processing.
Implemented a cooperative module integrating POSConvEmbedding and single-head self-attention for integrating spatial and contextual features.
Performed experiments on SIDD and HIT-UAV datasets to evaluate model performance.
Achieved mAP scores of 97.2% on SIDD-City, 82.6% on SIDD-Mountain, and 82.8% on HIT-UAV, improving 2.8% to 14.1% over YOLOv10.
Increased F1 score by 14.8%, indicating better target detection accuracy.
Maintained low computational cost at 27.9 GFLOPs while achieving real-time performance at 42.8 FPS.

Abstract

Infrared Small Target Detection (IRSTD) holds significant application value in military operations, early warning and surveillance, aerospace, and other fields. However, traditional detection methods face challenges related to small target pixel sizes, strong background noise, and sparse infrared image features, resulting in insufficient accuracy and robustness. This paper proposes IR-WSANet, a lightweight network based on an improved YOLOv10, which enhances the detection performance of infrared small targets through a frequency-spatial joint optimization strategy. Firstly, the discrete wavelet transform convolution (DWaveletConv) is introduced into the backbone network, and the fusion of high-frequency details and low-frequency semantics is enhanced by multi-band feature decomposition to suppress noise interference. Secondly, we designed a cooperative module (POS-SHSA) that integrates POSConvEmbedding with a partial channel single-head self-attention mechanism (SHSA), which combines local spatial features and global context information to improve the positioning accuracy of small targets. Experiments verify the effectiveness of the model on SIDD and HIT-UAV datasets: the mAP of IR-WSANet on SIDD-City, SIDD-Mountain and HIT-UAV datasets reaches 97.2%, 82.6% and 82.8%, respectively, which is 2.8% to 14.1% higher than the baseline YOLOv10, and the highest F1 score was improved to 14.8%, while maintaining low computing cost (27.9 GFLOPs) and real-time performance (42.8 FPS). The results show that IR-WSANet significantly improves the detection performance of infrared small targets in complex scenes through the combination of frequency domain filtering enhancement and space-channel dual attention mechanism.

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