Liu Gang, Xi Jiangtao, Ma Chao, Chen Huixiang
College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia.
Sensors (Basel). 2024 Dec 11;24(24):7933. doi: 10.3390/s24247933.
In order to achieve infrared aircraft detection under interference conditions, this paper proposes an infrared aircraft detection algorithm based on high-resolution feature-enhanced semantic segmentation network. Firstly, the designed location attention mechanism is utilized to enhance the current-level feature map by obtaining correlation weights between pixels at different positions. Then, it is fused with the high-level feature map rich in semantic features to construct a location attention feature fusion network, thereby enhancing the representation capability of target features. Secondly, based on the idea of using dilated convolutions to expand the receptive field of feature maps, a hybrid atrous spatial pyramid pooling module is designed. By utilizing a serial structure of dilated convolutions with small dilation rates, this module addresses the issue of feature information loss when expanding the receptive field through dilated spatial pyramid pooling. It captures the contextual information of the target, further enhancing the target features. Finally, a dice loss function is introduced to calculate the overlap between the predicted results and the ground truth labels, facilitating deep excavation of foreground information for comprehensive learning of samples. This paper constructs an infrared aircraft detection algorithm based on a high-resolution feature-enhanced semantic segmentation network which combines the location attention feature fusion network, the hybrid atrous spatial pyramid pooling module, the dice loss function, and a network that maintains the resolution of feature maps. Experiments conducted on a self-built infrared dataset show that the proposed algorithm achieves a mean intersection over union (mIoU) of 92.74%, a mean pixel accuracy (mPA) of 96.34%, and a mean recall (MR) of 96.19%, all of which outperform classic segmentation algorithms such as DeepLabv3+, Segformer, HRNetv2, and DDRNet. This demonstrates that the proposed algorithm can achieve effective detection of infrared aircraft in the presence of interference.
为了在干扰条件下实现红外飞机检测，本文提出了一种基于高分辨率特征增强语义分割网络的红外飞机检测算法。首先，利用设计的位置注意力机制，通过获取不同位置像素之间的相关权重来增强当前层特征图。然后，将其与富含语义特征的高层特征图融合，构建位置注意力特征融合网络，从而增强目标特征的表示能力。其次，基于使用空洞卷积扩大特征图感受野的思想，设计了一种混合空洞空间金字塔池化模块。该模块通过利用小扩张率的空洞卷积串行结构，解决了通过空洞空间金字塔池化扩大感受野时特征信息丢失的问题。它捕获目标的上下文信息，进一步增强目标特征。最后，引入骰子损失函数来计算预测结果与真实标签之间的重叠，便于深入挖掘前景信息以全面学习样本。本文构建了一种基于高分辨率特征增强语义分割网络的红外飞机检测算法，该网络结合了位置注意力特征融合网络、混合空洞空间金字塔池化模块、骰子损失函数以及保持特征图分辨率的网络。在自建红外数据集上进行的实验表明，所提算法的平均交并比（mIoU）达到92.74%，平均像素精度（mPA）达到96.34%，平均召回率（MR）达到96.19%，均优于DeepLabv3+、Segformer、HRNetv2和DDRNet等经典分割算法。这表明所提算法能够在存在干扰的情况下实现对红外飞机的有效检测。
