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基于改進(jìn)YOLOv7的室內摔倒行為檢測
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西安工程大學(xué) 計算機科學(xué)學(xué)院

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TP391.41

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Indoor fall behavior detection algorithm based on improved YOLOv7
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    摘要:

    針對室內監控視頻中老年人摔倒行為的檢測問(wèn)題,提出一種基于改進(jìn) YOLOv7 網(wǎng)絡(luò )模型的實(shí)時(shí)摔倒行為檢測算法。基于YOLOv7的目標檢測模型傳統使用跨步卷積來(lái)實(shí)現下采樣特征,但這可能會(huì )使目標信息的特征模糊。為了解決這個(gè)問(wèn)題,引入了新的下采樣模塊——魯棒特征下采樣,以改善下采樣過(guò)程中目標信息特征的清晰度。此外,通過(guò)在網(wǎng)絡(luò )的 concat 部分引入 CoordAttention 注意力機制,可更好地融合拼接后的特征圖。實(shí)驗結果表明,改進(jìn)后的YOLOv7模型在摔倒行為檢測方面具有較高的準確率和檢測性能,準確率達到98.88%,mAP50值達到98.83%,mAP50-95值達到74.12%。這意味著(zhù)該算法可以準確地檢測老年人的摔倒行為,家人能夠及時(shí)地發(fā)現,以便及時(shí)采取必要的救助措施。

    Abstract:

    Aiming at the problem of fall behavior detection in indoor surveillance video, a real-time fall behavior detection algorithm based on improved YOLOv7 network model was proposed. The target detection model based on YOLOv7 traditionally uses strided convolution to realize the feature of downsampling, but this may make the feature of the target information fuzzy. To solve this problem, a new downsampling module, robust feature downsampling, is introduced to improve the clarity of target information features during downsampling. In addition, by introducing CoordAttention attention mechanism in the concat portion of the network, the spliced feature graphs can be better merged. The experimental results show that the improved YOLOv7 model has a high accuracy and detection performance in fall behavior detection, with the accuracy reaching 98.88%, the mAP50 value reaching 98.83%, and the mAP50-95 value reaching 74.12%.This means that the algorithm can accurately detect the fall behavior of the elderly, and the family can find it in time so that the necessary rescue measures can be taken in time.

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陳華艷,張曉濱.基于改進(jìn)YOLOv7的室內摔倒行為檢測計算機測量與控制[J].,2024,32(12):35-42.

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歷史
  • 收稿日期:2023-11-01
  • 最后修改日期:2023-12-13
  • 錄用日期:2023-12-14
  • 在線(xiàn)發(fā)布日期: 2024-12-24
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