Infrared Image Small-Target Detection Based on Improved FCOS and Spatio-Temporal Features

The research of infrared image small-target detection is of great significance to security monitoring, satellite remote sensing, infrared early warning, and precision guidance systems. However, small infrared targets occupy few pixels and lack color and texture features, which make the detection of small infrared targets extremely challenging. This paper proposes an effective single-stage infrared small-target detection method based on improved FCOS (Fully Convolutional One-Stage Object Detection) and spatio-temporal features. In view of the simple features of infrared small targets and the requirement of real-time detection, based on the standard FCOS network, we propose a lightweight network model combined with traditional filtering methods, whose response for small infrared targets is enhanced, and the background response is suppressed. At the same time, in order to eliminate the influence of static noise points in the infrared image on the detection of small infrared targets, time domain features are added to the improved FCOS network in the form of image sequences, so that the network can learn the spatio-temporal correlation features in the image sequence. Finally, compared with current typical infrared small-target detection methods, the comparative experiments show that the improved FCOS method proposed in this paper had better detection accuracy and real-time performance for infrared small targets.

[1]  Haibo Luo,et al.  ISTDet: An efficient end-to-end neural network for infrared small target detection , 2021 .

[2]  Yimian Dai,et al.  Attentional Local Contrast Networks for Infrared Small Target Detection , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Yimian Dai,et al.  Asymmetric Contextual Modulation for Infrared Small Target Detection , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).

[4]  Bin Zhao,et al.  A Novel Pattern for Infrared Small Target Detection With Generative Adversarial Network , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Gaofeng Meng,et al.  Stitcher: Feedback-driven Data Provider for Object Detection , 2020, ArXiv.

[6]  Liyuan Liu,et al.  TBC-Net: A real-time detector for infrared small target detection using semantic constraint , 2019, ArXiv.

[7]  Huan Wang,et al.  Infrared Dim and Small Target Detection Based on Denoising Autoencoder Network , 2019, Mobile Networks and Applications.

[8]  Luping Zhou,et al.  Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation in Infrared Images , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[9]  Zhenming Peng,et al.  Infrared Small Target Detection by Density Peaks Searching and Maximum-Gray Region Growing , 2019, IEEE Geoscience and Remote Sensing Letters.

[10]  Hao Chen,et al.  FCOS: Fully Convolutional One-Stage Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[11]  Kyunghyun Cho,et al.  Augmentation for small object detection , 2019, 9th International Conference on Advances in Computing and Information Technology (ACITY 2019).

[12]  Zhaoxiang Zhang,et al.  Scale-Aware Trident Networks for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[13]  Yongqiang Zhang,et al.  SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network , 2018, ECCV.

[14]  Larry S. Davis,et al.  SNIPER: Efficient Multi-Scale Training , 2018, NeurIPS.

[15]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[16]  Yichen Wei,et al.  Relation Networks for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[17]  Larry S. Davis,et al.  An Analysis of Scale Invariance in Object Detection - SNIP , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[18]  Yunhong Wang,et al.  Receptive Field Block Net for Accurate and Fast Object Detection , 2017, ECCV.

[19]  Shifeng Zhang,et al.  Single-Shot Refinement Neural Network for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Р Ю Чуйков,et al.  Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector , 2017 .

[22]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[23]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[24]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Yi Yang,et al.  Infrared Patch-Image Model for Small Target Detection in a Single Image , 2013, IEEE Transactions on Image Processing.

[26]  Fei Zhang,et al.  Edge directional 2D LMS filter for infrared small target detection , 2012 .

[27]  Meng Hwa Er,et al.  Max-mean and max-median filters for detection of small targets , 1999, Optics & Photonics.

[28]  Tamar Peli,et al.  Morphology-based algorithm for point target detection in infrared backgrounds , 1993, Defense, Security, and Sensing.