| DC 欄位 |
值 |
語言 |
| DC.contributor | 通訊工程學系 | zh_TW |
| DC.creator | 呂政穆 | zh_TW |
| DC.creator | Cheng-Mu Lu | en_US |
| dc.date.accessioned | 2024-7-15T07:39:07Z | |
| dc.date.available | 2024-7-15T07:39:07Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=111523031 | |
| dc.contributor.department | 通訊工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 隨著網路世界高速發展，多種社群平台走進大眾的視野之中，像是
Youtube 和 TikTok，在物聯網下比起以往的文字貼文，影音的影響更是大
幅的增加資訊的普及性，無論是各個學術領域的知識亦或是新聞資訊的熱
門影片，搭配短影片的出現更是加速傳播的速度，導致許多未成年人可能
會持續認知這些偏頗的資訊且未能被及時糾正，進而導向錯誤的價值觀。
本論文提出一個使用 YOLO 透過增量式訓練搭配不同優化器組合選
擇，針對危險物品訓練出高準確率的偵測模型，透過對螢幕畫面進行擷取
的方式，使其可以針對多場景下的影像進行即時偵測並辨識，像是針對
Twitch 串流平台上和 Youtube 影音平台上的影像，透過辨識出危險物品
後，對其進行圖像擷取並加以標註圖像訊息，透過 Line Notify 進行通報使
用者。
本系統能夠縮減各種硬體上的限制，不同於以往透過監視器進行人為
審查的部分，能大幅降低所需的人力成本和時間成本，並且高準確率能減
少人為篩檢的錯誤概率，在實際操作上也展現出其即時性和高準確率，這
種偵測模式不單單受限於家長監護管理，也適用於任何需要偵測的場景。 | zh_TW |
| dc.description.abstract | With the rapid development of the internet, various social platforms like
YouTube and TikTok have come into the public eye. Under the Internet of
Things, the impact of videos has significantly increased the dissemination of
information. This applies to academic fields of knowledge and popular news.
The advent of short videos has further accelerated the spread of information,
leading to many minors potentially continuously absorbing biased information
without timely correction, which can lead to distorted values.
This paper proposes a high-accuracy detection model for dangerous objects
trained using YOLO through incremental training with different Optimizer
combinations. By capturing screen images, this system can perform real-time
detection and recognition of images across multiple scenarios, such as on
various social platforms. Upon recognizing dangerous objects, the system
captures and annotates the images, and notifies users through Line Notify.
This system reduces various hardware limitations from traditional manual
monitoring through surveillance cameras, significantly reducing required human
and time costs. The high accuracy reduces the probability of errors. In practical
application, it demonstrates its real-time capability and high accuracy. This
detection model is not only limited to parental supervision but is also applicable
to any scenario requiring detection. | en_US |
| DC.subject | YOLO | zh_TW |
| DC.subject | 物聯網 | zh_TW |
| DC.subject | 增量式訓練 | zh_TW |
| DC.subject | 優化器組合 | zh_TW |
| DC.subject | 即時影像偵測 | zh_TW |
| DC.subject | YOLO | en_US |
| DC.subject | IoT | en_US |
| DC.subject | Incremental Learning | en_US |
| DC.subject | Optimizer combinations | en_US |
| DC.subject | Real-time detection | en_US |
| DC.title | 基於 YOLO 演算法在多種場景下的危險物品即時偵測 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Real-Time Detection of Danger Objects in Various Scenes Based on the YOLO Algorithm | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |