JP2018113038A - 検査機器および荷物における銃器を検出する方法 - Google Patents
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Abstract
【解決手段】荷物に対してX線検査を行い、透過画像を取得するステップと、訓練された銃器検出神経回路網に基づいて、透過画像における複数の候補領域を特定するステップと、銃器検出神経回路網に基づいて、複数の候補領域を分類することによって、透過画像に銃器が含まれているか否かを特定するステップと、を含む。銃器が検出されると、画像に標記することで、操作者に通知して、人工的に画像を判定する作業量を減少することができる。
【選択図】図6
Description
Claims (10)
- 荷物に対してX線検査を行い、透過画像を取得するステップと、
訓練された銃器検出神経回路網に基づいて、前記透過画像における複数の候補領域を特定するステップと、
前記銃器検出神経回路網に基づいて、前記複数の候補領域を分類することによって、前記透過画像に銃器が含まれているか否かを特定するステップと、を含む荷物における銃器を検出する方法。 - 各候補領域に銃器が含まれる信頼度を算出すると共に、前記信頼度が所定の閾値よりも大きい場合、前記候補領域に銃器が含まれていると判断する、請求項1に記載の荷物における銃器を検出する方法。
- 複数の候補領域のそれぞれに同じ銃器が含まれる場合、各候補領域における銃器の画像を標記するとともに、銃器の位置を取得するために、前記複数の候補領域における銃器の画像を統合する、請求項1に記載の荷物における銃器を検出する方法。
- 前記銃器検出神経回路網は、銃器サンプルの透過画像を構築する操作と、RPNとCNNの畳み込み層を統合して初期検出ネットワークを取得する操作と、サンプルの透過画像に基づいて初期検出ネットワークを訓練して、銃器検出神経回路網を取得する操作とによって訓練されて得たものである、請求項1に記載の荷物における銃器を検出する方法。
- 初期検出ネットワークを訓練するステップは、
RPNとCNNとの間で畳み込み層のデータを共有しない場合、サンプルの透過画像から特定された複数のサンプル候補領域によって初期検出ネットワークを調整するステップと、
RPNとCNNとの間で畳み込み層のデータを共有する場合、RPNを訓練するステップと、
RPNとCNNとの間で共用する畳み込み層のデータを変えずに、前記初期検出ネットワークを収束するまで調整して、銃器検出神経回路網を取得するステップと、を含む、請求項4に記載の荷物における銃器を検出する方法。 - 前記初期検出ネットワークを訓練するステップは、複数のサンプル候補領域から、人工的に標記された銃器の矩形枠との重なり面積が閾値より小さいものを削除するステップをさらに含む、請求項5に記載の荷物における銃器を検出する方法。
- 荷物に対してX線検査を行い、透過画像を取得するX線検査システムと、
前記透過画像を格納するメモリと、
訓練された銃器検出神経回路網に基づいて、前記透過画像における複数の候補領域を特定し、且つ前記複数の候補領域を分類することによって、前記透過画像に銃器が含まれているか否かを特定するように構成されたプロセッサと、を備える検査機器。 - 前記プロセッサは、各候補領域に銃器が含まれる信頼度を算出するとともに、前記信頼度が所定の閾値よりも大きい場合、前記候補領域に銃器が含まれていると判断するように配置されている、請求項7に記載の検査機器。
- 前記プロセッサは、複数の候補領域のそれぞれに同一の銃器が含まれる場合、各候補領域における銃器の画像を標記すると共に、銃器の位置を得るために、前記複数の候補領域における銃器の画像を統合するように配置されている、請求項7に記載の検査機器。
- 前記メモリには、銃器サンプルの透過画像が格納されており、
前記プロセッサは、RPNとCNNの畳み込み層を統合して初期検出ネットワークを取得する操作と、サンプルの透過画像に基づいて初期検出ネットワークを訓練して、銃器検出神経回路網を取得する操作とによって訓練されて、前記銃器検出神経回路網を取得するように配置されている、請求項7に記載の検査機器。
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