ECMSFNet: Real-Time Infrared Small Target Detection Network With Efficient Convolution and Multiscale Feature Fusion

Le résumé fourni par la source

With the continuous development of fields such as national defense and military applications, the importance of infrared small target detection (IRSTD) technology based on thermal imaging has become increasingly prominent. However, in practical application scenarios, it remains difficult to effectively extract the features of weak and small targets under low signal-to-noise ratio conditions, while simultaneously suppressing background clutter, preserving target details, and balancing detection accuracy and speed. To address the issues mentioned above, this work proposes a real-time IRSTD network (ECMSFNet) based on efficient convolution and attention-guided multi-scale feature weighting and fusion. During the feature extraction stage, a dual-branch hybrid convolution module (DBHConv) is designed to extract infrared small target features more efficiently. In the feature fusion stage, a three-branch attention-guided module (TBAG) is designed to enhance the input features from both spatial and channel dimensions. By using a three-branch parallel structure to process input features in a differentiated manner, noise is effectively filtered while target detail information is preserved. In addition, to further address the issue of missed detections, a multi-scale feature weighting and fusion module (MSFWF) is designed at the added detection head to adaptively weight the features and optimize the feature propagation path, thereby improving the model’s detection accuracy. Extensive experimental results on multiple datasets demonstrate that the method proposed in this paper outperforms other advanced approaches and achieves a real-time detection speed of 74.63 frames per second. https://github.com/liubiaohua/ECMSFNet.

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