Zhang R Z, Zhang J L, Qi X P, et al. Infrared target detection and recognition in complex scene[J]. Opto-Electron Eng, 2020, 47(10): 200314. doi: 10.12086/oee.2020.200314
Citation: Zhang R Z, Zhang J L, Qi X P, et al. Infrared target detection and recognition in complex scene[J]. Opto-Electron Eng, 2020, 47(10): 200314. doi: 10.12086/oee.2020.200314

Infrared target detection and recognition in complex scene

    Fund Project: Supported by National High Technology Research Development Program China (G158207)
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  • The mainstream target detection network has outstanding target detection capability in high quality RGB images, but for infrared images with poor resolution, the target detection performance decreases significantly. In order to improve the performance of infrared target detection in complex scene, the following measures are adopted in this paper: Firstly, by referring to the field adaption and adopting the appropriate infrared image preprocessing means, the infrared image is closer to the RGB image, so that the mainstream target detection network can further improve the detection accuracy. Secondly, based on the one-stage target detection network YOLOv3, the algorithm replaces the original MSE loss function with the GIOU loss function. It is verified by experiments that the detection accuracy on the open infrared data set the FLIR is significantly improved. Thirdly, in view of the problem of large target size span existing in FLIR dataset, the SPP module is added with reference to the idea of the spatial pyramid to enrich the expression ability of feature map, expand the receptive field of feature map, and further improve the accuracy of target detection.
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  • Overview: In recent years, with the continuous development of computer vision, the ability of target detection based on deep learning has been significantly improved. However, most of the images used by mainstream target detection networks are RGB images, and there are few studies on the direction of infrared target detection. Moreover, the mainstream target detection network has a prominent target detection capability in high quality RGB images, but the target detection performance in infrared images with poor resolution is significantly reduced. Compared with infrared images, visible images have higher imaging resolution and rich target detail information. However, under certain weather conditions, the visible images cannot be obtained. Infrared imaging technology has the characteristics of long range, strong anti-interference ability, high measurement accuracy, not affected by weather, able to work day and night, and strong ability to penetrate smoke. Therefore, infrared imaging technology has been widely used once it was proposed. The demand for infrared target detection is also urgent.

    In order to improve the performance of infrared target detection in complex scenes, the following measures are adopted in this paper: First, referring to the field adaptive method, appropriate infrared image preprocessing means are adopted to make the infrared image closer to the RGB image, so as to further improve the detection accuracy by applying the mainstream target detection network. Secondly, mean square error (MSE), a loss function, regards the coordinate value of each point of BBox as an independent variable, which does not consider the integrity of the target frame, and ln-norms is sensitive to the scale of the object, so the algorithm is based on the single-stage target detection network YOLOv3 and replaces the original MSE loss function with GIOU loss function. It is verified by experiments that the detection accuracy on FLIR, an open infrared data set, is significantly improved, and the problem of inaccurate location in the original network is effectively improved. Thirdly, in view of the problem of large span of target size in the FLIR data set, the SPP module is added to enrich the expression ability of feature map and expand the receptive field of feature map by referring to the idea of space pyramid. The experimental results show that the network detection error rate decreases after the addition of SPP module, and after overcoming the original deficiency of the YOLOv3, the target accuracy of detection can be further improved compared with the modification of GIOU loss function only.

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    沈阳化工大学材料科学与工程学院 沈阳 110142

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