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遠紅外車載圖像實時行人檢測與自適應實例分割

Real-Time Pedestrian Detection for Far-Infrared Vehicle Images and Adaptive Instance Segmentation

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摘要

針對紅外圖像檢測與分割任務中顏色信息缺失,特征細節模糊并帶有噪聲,當目標數量較多時傳統方法提取過程速度較慢等問題,提出一種用于遠紅外圖像的優化YOLO檢測與分割網絡模型。提出的兩個優化點分別為:綜合分析實驗使用的兩種遠紅外數據集后使用K-means++聚類算法尋找多尺度預測標記錨點框尺寸;使用局部檢測位置自適應閾值分割方法對檢測目標進行像素級實例分割。本文優化算法在FLIR公開數據集與本文數據集中的檢測速度分別為29 frame/s與28 frame/s,保證了實時輸出的要求;行人檢測準確率分別達到75.3%與77.6%,分割結果平均交并比達到70%~90%。實驗結果表明,本文算法具有良好的穩健性和普適性,在遠紅外圖像中可快速有效地檢測行人并生成實例掩模。

Abstract

In infrared image detection and segmentation tasks, the color information is lost, the features are fuzzy with noise, the target number is large, and the traditional extraction method is slow. Therefore, we propose an optimized YOLO detection and segmentation network model for far-infrared images. The two proposed optimization points are as follows. We use the K-means++ clustering algorithm to determine the multi-scale prediction anchor size after the analysis of two far-infrared databases. We also perform pixel-level instance segmentation of detection targets using localized adaptive threshold segmentation. The experimental results show that the proposed algorithm performs pedestrian detection at detection speeds of 29 frame/s and 28 frame/s on the FLIR dataset and the dataset used in this paper, respectively, ensuring the requirement of real-time output. The pedestrian detection accuracies in these datasets reach 75.3% and 77.6%. Moreover, the average intersection over the union of the segmentation results is 70%--90%. In summary, the algorithm performs well with respect to robustness and universality. The algorithm provides a valuable reference method for pedestrian detection and segmentation in far-infrared fields.

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補充資料

中圖分類號:TP183

DOI:10.3788/LOP57.021507

所屬欄目:機器視覺

基金項目:國家自然科學基金;

收稿日期:2019-04-17

修改稿日期:2019-07-09

網絡出版日期:2020-01-01

作者單位    點擊查看

于博:大連海事大學信息科學技術學院, 遼寧 大連 116026
馬書浩:大連海事大學信息科學技術學院, 遼寧 大連 116026
李紅艷:大連海事大學信息科學技術學院, 遼寧 大連 116026
李春庚:大連海事大學信息科學技術學院, 遼寧 大連 116026
安居白:大連海事大學信息科學技術學院, 遼寧 大連 116026

聯系人作者:李春庚(li_chungeng@dlmu.edu.cn)

備注:國家自然科學基金;

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引用該論文

Yu Bo,Ma Shuhao,Li Hongyan,Li Chungeng,An Jubai. Real-Time Pedestrian Detection for Far-Infrared Vehicle Images and Adaptive Instance Segmentation[J]. Laser & Optoelectronics Progress, 2020, 57(2): 021507

于博,馬書浩,李紅艷,李春庚,安居白. 遠紅外車載圖像實時行人檢測與自適應實例分割[J]. 激光與光電子學進展, 2020, 57(2): 021507

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