Liang Zimeng, Shen Hua
National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel). 2024 Dec 4;24(23):7767. doi: 10.3390/s24237767.
Infrared small target detection technology has been widely applied in the defense sector, including applications such as precision targeting, alert systems, and naval monitoring. However, due to the small size of their targets and the extended imaging distance, accurately detecting drone targets in complex infrared environments remains a considerable challenge. Detecting drone targets accurately in complex infrared environments poses a substantial challenge. This paper introduces a novel model that integrates edge characteristics with multi-scale feature fusion, named Edge-Guided Feature Pyramid Networks (EG-FPNs). This model aims to capture deep image features while simultaneously emphasizing edge characteristics. The goal is to resolve the problem of missing target information that occurs when Feature Pyramid Networks (FPNs) perform continuous down-sampling to obtain deeper semantic features. Firstly, an improved residual block structure is proposed, integrating multi-scale convolutional feature extraction and inter-channel attention mechanisms, with significant features being emphasized through channel recalibration. Then, a layered feature fusion module is introduced to strengthen the shallow details in images while fusing multi-scale image features, thereby strengthening the shallow edge features. Finally, an edge self-fusion module is proposed to enhance the model's depiction of image features by extracting edge information and integrating it with multi-scale features. We conducted comparative experiments on multiple datasets using the proposed algorithm and existing advanced methods. The results show improvements in the IoU, nIoU, and F1 metrics, while also showcasing the lightweight nature of EG-FPNs, confirming that they are more suitable for drone detection in resource-constrained infrared scenarios.
红外小目标检测技术已在国防领域得到广泛应用，包括精确瞄准、警报系统和海军监测等应用。然而，由于目标尺寸小且成像距离远，在复杂红外环境中准确检测无人机目标仍然是一项重大挑战。在复杂红外环境中准确检测无人机目标是一项重大挑战。本文介绍了一种将边缘特征与多尺度特征融合相结合的新型模型，名为边缘引导特征金字塔网络（EG-FPN）。该模型旨在捕捉深度图像特征，同时强调边缘特征。目的是解决特征金字塔网络（FPN）在进行连续下采样以获得更深层语义特征时出现的目标信息丢失问题。首先，提出了一种改进的残差块结构，集成了多尺度卷积特征提取和通道间注意力机制，通过通道重新校准突出重要特征。然后，引入了分层特征融合模块，在融合多尺度图像特征的同时增强图像中的浅层细节，从而强化浅层边缘特征。最后，提出了一种边缘自融合模块，通过提取边缘信息并将其与多尺度特征集成来增强模型对图像特征的描绘。我们使用所提出的算法和现有先进方法在多个数据集上进行了对比实验。结果显示在交并比（IoU）、归一化交并比（nIoU）和F1指标上有所改进，同时也展示了EG-FPN的轻量级特性，证实它们更适合在资源受限的红外场景中进行无人机检测。
