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基于深度歸一化的任意交互物體檢測方法研究
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吉林省重點研發(fā)計劃項目(20200401130GX)和國家自然科學(xué)基金項目(51575219)


Arbitrary Interactive Object Detection Method Based on Deep Normalization
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    摘要:

    交互物體的檢測識別是實現(xiàn)人機交互的一項關(guān)鍵技術(shù),針對人機交互過程中交互物體檢測范圍受限的問題,本文利用深度歸一化提高深度圖像質(zhì)量,提出了一種基于圖像分割的任意交互物體檢測方法。該方法針對操作人員側(cè)向和正向姿態(tài),分別采用基于顯著性檢測的圖像處理和人體姿態(tài)引導(dǎo)的區(qū)域生長算法分割目標區(qū)域,錨定目標物體邊框?qū)崿F(xiàn)物體檢測。最后,進行了交互物體檢測實驗及不同深度區(qū)間位置測距和跟隨實驗。實驗結(jié)果表明,所提出的物體檢測方法能夠?qū)崿F(xiàn)任意交互物體檢測,在交互物體檢測方面具有廣泛適用性;較小深度區(qū)間的歸一化能夠使物體位置誤差變小,提高了物體檢測距離精度及機器人跟隨效果。

    Abstract:

    Detection and recognition of interactive objects is a key technology to realize human-computer interaction, and in order to solve the problem of limited types of interactive objects in the process of human-computer interaction, an arbitrary interactive object detection method was proposed based on image segmentation. Firstly, for the depth image, after filtering out the data outside the range of the original depth data, the min-max scale normalization method was used to improve the quality of the depth image. Secondly, the target area was segmented by using the image processing method based on saliency detection and the human pose-guided region growth algorithm for the operator’s side-to-side camera and front-facing camera posture, respectively. Then, the pixel set of the target object obtained by the above segmentation was input into the image processing functions, and the minimum external rectangle of the area point set was obtained, and the rotating bounding box of the target object was anchored. Then, for the depth image, after filtering out the data outside the range of the original depth data, the min-max scale normalization method was used to improve the quality of the depth image. Finally, the detection experiments of arbitrary interactive objects and the ranging and following experiments of different depth intervals were carried out. Experimental results showed that the proposed object detection method had a lower detection cost and a higher degree of freedom in the detection category of interactive objects, which can realize the detection of arbitrary interactive objects, and had wide applicability in the detection of interactive objects. The normalization of the small depth interval can effectively improve the depth image quality, make the object position error smaller, and improve the accuracy of the object detection distance and the following effect of the robot in the human-computer interaction experiment.

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黃玲濤,孔紫靜,楊帆,張紅彥.基于深度歸一化的任意交互物體檢測方法研究[J].農(nóng)業(yè)機械學(xué)報,2024,55(8):428-436. HUANG Lingtao, KONG Zijing, YANG Fan, ZHANG Hongyan. Arbitrary Interactive Object Detection Method Based on Deep Normalization[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(8):428-436.

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  • 收稿日期:2024-04-17
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  • 在線發(fā)布日期: 2024-08-10
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