TWI717043B - System and method for recognizing aquatic creature - Google Patents
System and method for recognizing aquatic creature Download PDFInfo
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Abstract
Description
本發明關於一種水下生物辨識系統及水下生物辨識方法,尤指一種可幫助使用者快速地自超音波影像辨識出水下生物之水下生物辨識系統及水下生物辨識方法。 The present invention relates to an underwater biological identification system and an underwater biological identification method, in particular to an underwater biological identification system and an underwater biological identification method that can help a user to quickly identify underwater creatures from ultrasonic images.
由於超音波具有不破壞材料結構以及不傷害生物體的特性,因而普遍地被應用於水下生物的偵測。超音波可以讓使用者觀察水下生物而不需直接潛入水中。然而,沒有受過專業訓練的一般使用者並無法直接從超音波影像辨識出水下生物的種類。因此,如何幫助使用者快速地自超音波影像辨識出水下生物,便成為一個重要的研究課題。 Since ultrasound has the characteristics of not destroying the material structure and not harming the organism, it is widely used in the detection of underwater creatures. Ultrasonic waves allow users to observe underwater creatures without diving directly into the water. However, ordinary users without professional training cannot directly identify the types of underwater creatures from ultrasonic images. Therefore, how to help users quickly identify underwater creatures from ultrasound images has become an important research topic.
本發明的目的之一在於提供一種可幫助使用者快速地自超音波影像辨識出水下生物之水下生物辨識系統及水下生物辨識方法,以解決上述問題。 One of the objectives of the present invention is to provide an underwater biological identification system and underwater biological identification method that can help users quickly identify underwater creatures from ultrasonic images, so as to solve the above-mentioned problems.
根據一實施例,本發明之水下生物辨識系統包含一水下超音波裝置、一處理器以及一混合型神經網路。水下超音波裝置朝一水下生物發射複數個超音波訊號,且接收複數個超音波訊號之複數個反射訊號。處理器根據複數個反射訊號產生一超音波影像。混合型神經網路包含關於水下生物之複數個自我訓練特徵以及複數個人工設定特徵。混合型神經網路根據複數個自我訓練特徵與複數個人工設定特徵,自超音波影像辨識出水下生物。 According to one embodiment, the underwater biological recognition system of the present invention includes an underwater ultrasonic device, a processor, and a hybrid neural network. The underwater ultrasonic device transmits a plurality of ultrasonic signals to an underwater creature, and receives a plurality of reflection signals of the plurality of ultrasonic signals. The processor generates an ultrasonic image based on a plurality of reflected signals. The hybrid neural network contains a plurality of self-training features and a plurality of artificial setting features about underwater creatures. The hybrid neural network recognizes underwater creatures from ultrasonic images based on multiple self-training features and multiple artificially set features.
根據另一實施例,本發明之水下生物辨識方法包含下列步驟:以一 水下超音波裝置朝一水下生物發射複數個超音波訊號,且接收複數個超音波訊號之複數個反射訊號;根據複數個反射訊號產生一超音波影像;將超音波影像輸入一混合型神經網路,其中混合型神經網路包含關於水下生物之複數個自我訓練特徵以及複數個人工設定特徵;以及由混合型神經網路根據複數個自我訓練特徵與複數個人工設定特徵,自超音波影像辨識出水下生物。 According to another embodiment, the underwater biological identification method of the present invention includes the following steps: The underwater ultrasonic device emits a plurality of ultrasonic signals to an underwater creature, and receives a plurality of reflection signals of the plurality of ultrasonic signals; generates an ultrasonic image based on the plurality of reflection signals; inputs the ultrasonic image into a hybrid neural network Road, where the hybrid neural network includes multiple self-training features and multiple artificial setting features about underwater creatures; and the hybrid neural network is based on the multiple self-training features and multiple artificial setting features from ultrasound images Identify underwater creatures.
綜上所述,本發明係利用混合型神經網路對超音波影像進行分析,以自超音波影像辨識出水下生物。由於混合型神經網路包含關於水下生物之複數個自我訓練特徵以及複數個人工設定特徵,因此,本發明可精準地對超音波影像中的水下生物進行辨識與分類,進而幫助使用者快速地自超音波影像辨識出水下生物。 In summary, the present invention uses a hybrid neural network to analyze ultrasonic images to identify underwater creatures from the ultrasonic images. Since the hybrid neural network includes a plurality of self-training features and a plurality of artificial setting features about underwater creatures, the present invention can accurately identify and classify underwater creatures in ultrasonic images, thereby helping users quickly The ground has identified underwater creatures from ultrasonic images.
關於本發明之優點與精神可以藉由以下的發明詳述及所附圖式得到進一步的瞭解。 The advantages and spirit of the present invention can be further understood from the following detailed description of the invention and the accompanying drawings.
1:水下生物辨識系統 1: Underwater biometric system
3:超音波影像 3: Ultrasonic image
10:水下超音波裝置 10: Underwater ultrasonic device
12:處理器 12: processor
14:混合型神經網路 14: Hybrid neural network
16:資料庫 16: Database
30:水下生物 30: Underwater creatures
32:標籤 32: label
S10-S16:步驟 S10-S16: steps
第1圖為根據本發明一實施例之水下生物辨識系統的功能方塊圖。 Figure 1 is a functional block diagram of an underwater biometric system according to an embodiment of the invention.
第2圖為根據本發明一實施例之超音波影像的示意圖。 FIG. 2 is a schematic diagram of an ultrasonic image according to an embodiment of the invention.
第3圖為根據本發明一實施例之水下生物辨識方法的流程圖。 Figure 3 is a flowchart of an underwater creature identification method according to an embodiment of the invention.
第4圖為第2圖中的水下生物之目前影像轉換為水下生物之實際影像的示意圖。 Figure 4 is a schematic diagram of the current image of underwater creatures in Figure 2 transformed into actual images of underwater creatures.
第5圖為以標籤標示水下生物的示意圖。 Figure 5 is a schematic diagram of labeling underwater creatures.
請參閱第1圖至第3圖,第1圖為根據本發明一實施例之水下生物辨識系統1的功能方塊圖,第2圖為根據本發明一實施例之超音波影像3的示意圖,第3圖為根據本發明一實施例之水下生物辨識方法的流程圖。第3圖中的水下生物辨
識方法可以第1圖中的水下生物辨識系統1來實現。
Please refer to Figures 1 to 3. Figure 1 is a functional block diagram of an underwater
如第1圖所示,水下生物辨識系統1包含一水下超音波裝置10、一處理器12、一混合型神經網路14以及一資料庫16。於此實施例中,處理器12、混合型神經網路14與資料庫16可設置於電腦(未顯示)中,且電腦可與水下超音波裝置10形成通訊,以進行訊號傳輸。水下超音波裝置10可為一水下超音波探頭或其它可收發超音波之水下超音波裝置。
As shown in FIG. 1, the underwater
當使用者欲以水下生物辨識系統1對一水下生物進行辨識時,使用者可先以水下超音波裝置10朝水下生物發射複數個超音波訊號,且接收複數個超音波訊號之複數個反射訊號(第3圖中的步驟S10)。接著,處理器12即會根據複數個反射訊號產生如第2圖所示之超音波影像3(第3圖中的步驟S12),其中超音波影像3中存在水下生物30(例如,魚、蝦、蟹等)。於此實施例中,超音波影像3可為經二值化處理後之二值化超音波影像,但不以此為限。需說明的是,二值化處理技術係為習知技藝之人所熟知,在此不再贅述。此外,超音波影像3中的水下生物之數量與種類係根據實際應用而決定,不以圖中所繪示之實施例為限。
When the user wants to identify an underwater creature with the underwater
接著,處理器12可將超音波影像3輸入混合型神經網路14(第3圖中的步驟S14),以對超音波影像3中的水下生物30進行辨識與分類。本發明之混合型神經網路14包含關於水下生物30之複數個自我訓練特徵以及複數個人工設定特徵。於此實施例中,混合型神經網路14可為卷積神經網路(Convolution Neural Network,CNN)或其它類似神經網路。於此實施例中,混合型神經網路14係已預先被訓練好,用以辨識水下生物。混合型神經網路14可預先對包含水下生物30之一影片進行影像辨識,以得到複數個自我訓練特徵。上述之影片可包含水下生物30之位置資訊、運動資訊、器官資訊等,視實際應用而定。需說明的是,神經網路之詳細訓練過程係為習知技藝之人所熟知,在此不再贅述。
Then, the
此外,複數個人工設定特徵可以手動方式輸入混合型神經網路14。
舉例而言,當水下生物為魚類時,人工設定特徵可包含魚體長度、魚體大小、游泳速度、魚鰾大小、活動深度、雷納數(Reynolds number)等,視實際應用而定。當使用者在超音波影像上發現新水下生物時,使用者可以自行計算並輸入上述之人工設定特徵至混合型神經網路14,以增加混合型神經網路14辨識水下生物的準確率。
In addition, a plurality of artificially set characteristics can be manually input into the hybrid
因此,在超音波影像3輸入混合型神經網路14後,即可由混合型神經網路14根據複數個自我訓練特徵與複數個人工設定特徵,自超音波影像3辨識出水下生物30(第3圖中的步驟S16)。
Therefore, after the
請參閱第4圖,第4圖為第2圖中的水下生物30之目前影像轉換為水下生物30之實際影像的示意圖。於此實施例中,本發明可進一步提供水下生物30之一實際影像,其中資料庫16可用以儲存水下生物30之實際影像。在混合型神經網路14自超音波影像3辨識出水下生物30後,處理器12可將超音波影像3中水下生物30之目前影像(如第2圖所示)轉換為水下生物30之實際影像(如第4圖所示)。藉此,使用者即可藉由水下生物30之實際影像快速地自超音波影像3辨識出水下生物30。
Please refer to Fig. 4, which is a schematic diagram of the current image of the
請參閱第5圖,第5圖為以標籤32標示水下生物30的示意圖。於此實施例中,本發明可進一步提供水下生物30之一標籤32(例如,魚類名稱),其中資料庫16可用以儲存水下生物30之標籤32。在混合型神經網路14自超音波影像3辨識出水下生物30後,處理器12可以標籤32標示水下生物30,使得使用者可藉由水下生物30之標籤32快速地自超音波影像3辨識出水下生物30。如第5圖所示,標籤32可標示於水下生物30之實際影像周圍。當然,標籤32亦可標示於水下生物30之實際影像上,視實際應用而定。此外,標籤32亦可標示於如第2圖所示之水下生物30之目前影像上或周圍,視實際應用而定。
Please refer to FIG. 5, which is a schematic diagram of marking
綜上所述,本發明係利用混合型神經網路對超音波影像進行分析, 以自超音波影像辨識出水下生物。由於混合型神經網路包含關於水下生物之複數個自我訓練特徵以及複數個人工設定特徵,因此,本發明可精準地對超音波影像中的水下生物進行辨識與分類,進而幫助使用者快速地自超音波影像辨識出水下生物。此外,本發明可進一步提供水下生物之實際影像及/或標籤,使得使用者可藉由水下生物之實際影像及/或標籤快速地自超音波影像辨識出水下生物。 In summary, the present invention uses a hybrid neural network to analyze ultrasound images, Identify underwater creatures with self-ultrasonic images. Since the hybrid neural network includes a plurality of self-training features and a plurality of artificial setting features about underwater creatures, the present invention can accurately identify and classify underwater creatures in ultrasonic images, thereby helping users quickly The ground has identified underwater creatures from ultrasonic images. In addition, the present invention can further provide actual images and/or tags of underwater creatures, so that users can quickly recognize underwater creatures from ultrasonic images by using the actual images and/or tags of underwater creatures.
以上所述僅為本發明之較佳實施例,凡依本發明申請專利範圍所做之均等變化與修飾,皆應屬本發明之涵蓋範圍。 The foregoing descriptions are only preferred embodiments of the present invention, and all equivalent changes and modifications made in accordance with the scope of the patent application of the present invention shall fall within the scope of the present invention.
1:水下生物辨識系統 1: Underwater biometric system
10:水下超音波裝置 10: Underwater ultrasonic device
12:處理器 12: processor
14:混合型神經網路 14: Hybrid neural network
16:資料庫 16: Database
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TW571248B (en) * | 2001-10-11 | 2004-01-11 | Exscientia Llc | Method and apparatus for learning to classify patterns and assess the value of decisions |
GB2522302A (en) * | 2013-10-31 | 2015-07-22 | Furuno Electric Co | Size-and-type determining device, underwater detecting apparatus and method of determining size and type |
WO2016205938A1 (en) * | 2015-06-22 | 2016-12-29 | Appetite Lab Inc. | Devices and methods for locating and visualizing underwater objects |
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TW571248B (en) * | 2001-10-11 | 2004-01-11 | Exscientia Llc | Method and apparatus for learning to classify patterns and assess the value of decisions |
GB2522302A (en) * | 2013-10-31 | 2015-07-22 | Furuno Electric Co | Size-and-type determining device, underwater detecting apparatus and method of determining size and type |
WO2016205938A1 (en) * | 2015-06-22 | 2016-12-29 | Appetite Lab Inc. | Devices and methods for locating and visualizing underwater objects |
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