基于多层卷积融合的红外小目标检测算法(特邀)

Infrared Small Target Detection via Multi-Layer Convolution Fusion(Invited)

红外小目标检测技术在多个关键领域,如自主导航、安防监控中具有重要的应用价值。该技术能够识别低光或遮挡环境中肉眼难以察觉的小目标,对于识别潜在威胁和提高遥感探测能力具有显著意义。然而,由于红外图像中小目标占据像素少且缺乏形状和纹理信息,精确检测红外小目标面临巨大挑战。为了克服这些难点,提出一种多层卷积融合模块与多感受野融合模块相结合的深度学习模型,旨在通过多层级的特征提取和不同感受野的特征融合,有效表征小目标。使用实验室拍摄的红外图像作为测试集。实验结果表明,所提模型在多个评价指标上表现优异,像素级的交并比达到0.814,同时样本级的交并比达到0.845,验证了模型在小目标检测任务中的高准确性和可靠性。为了评估不同模块对模型性能的影响,进行了消融实验,结果进一步证实了多层卷积融合模块和多感受野融合模块对提升模型性能的重要性。

Infrared small target detection technology holds important application value across key fields,such as autonomous navigation and security monitoring.This technology specializes in identifying small targets that are challenging to detect with the naked eyes,especially in low-light or obstructed environments.This functionality is of utmost importance for detecting potential threats and enhancing remote sensing capabilities.However,accurately detecting small infrared targets in infrared images presents substantial challenges due to their minimal pixel coverage and lack of shape and texture details.To address these challenges,we propose a deep learning model that integrates a multilayer convolutional fusion module and a multi-receptive field fusion module.The proposed model aims to effectively represent small targets by extracting features at multiple levels and fusing features from different receptive fields.The model is tested using infrared images captured in a laboratory setting.The experimental results demonstrat that the proposed model performed well across multiple evaluation indicators,achieving a pixel-level intersection-over-union ratio of 0.814 and a sample-level intersection-over-union ratio of 0.845.These results confirm the high accuracy and reliability of the model for small object detection tasks.Furthermore,ablation experiments are conducted to evaluate the influence of different modules on model performance.These experiments confirm that both the multilayer convolutional fusion and multireceptive field fusion modules play crucial roles in improving model performance.