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Abstract：
Infrared small target detection technology plays a crucial role in various fields such as military reconnaissance, power patrol, medical diagnosis, and security. The advancement of deep learning has led to the success of convolutional neural networks in target segmentation. However, due to challenges like small target scales, weak signals, and strong background interference in infrared images, convolutional neural networks often face issues like leakage and misdetection in small target segmentation tasks. To address this, an enhanced U-Net method called MST-UNet is proposed, the method combines multi-scale feature decomposition and fusion and attention mechanisms. The method involves using Haar wavelet transform instead of maximum pooling for downsampling in the encoder to minimize feature loss and enhance feature utilization. Additionally, a multi-scale residual unit is introduced to extract contextual information at different scales, improving sensory field and feature expression. The inclusion of a triple attention mechanism in the encoder structure further enhances multidimensional information utilization and feature recovery by the decoder. Experimental analysis on the NUDT-SIRST dataset demonstrates that the proposed method significantly improves target contour accuracy and segmentation precision, achieving IoU and nIoU values of 80.09% and 80.19%, respectively.
摘要：
红外小目标检测技术在军事侦察等各个领域发挥着至关重要的作用,电力巡逻,医学诊断,和安全。深度学习的进步导致了卷积神经网络在目标分割中的成功。然而,由于小目标规模等挑战，微弱信号,红外图像中的强背景干扰，卷积神经网络在小目标分割任务中经常面临泄漏和误检测等问题。为了解决这个问题，提出了一种名为MST-UNet的增强型U-Net方法，该方法结合了多尺度特征分解和融合以及注意力机制。该方法涉及使用Haar小波变换而不是最大池化来在编码器中进行下采样,以最小化特征损失并提高特征利用率。此外,引入多尺度残差单元来提取不同尺度的上下文信息，改善感官领域和特征表达。在编码器结构中包括三重注意机制进一步增强了解码器的多维信息利用和特征恢复。在NUDT-SIRST数据集上的实验分析表明,该方法显著提高了目标轮廓精度和分割精度,实现80.09%和80.19%的IoU和nIoU值，分别。
