汽機車的發明，縮短了世界上的距離，但是也帶來相當的危險性，車禍的發生會造成巨大的社會成本以及人民生命財產的危機，因此近些年來，車禍相關的研究逐漸增加。台灣地小人稠，車禍的傷亡度亦為世界上最高的國家之一，因此在處理車禍相關問題勢在必行。大多數相關文獻多為處理車上傳感器的資訊，分析是否發生車禍，但是其無法判斷車禍的肇事責任，因此我們使用深度學習物體辨識方法Faster Region-based Convolutional Neural Network(Faster R-CNN)進行即時的車禍辨識，並且將其實作於Android系統上。
首先我們在電腦上訓練Faster R-CNN模型，接著將訓練好的模型轉為tflite型態以便Android系統能夠使用。然後透過Andorid Studio編寫應用程式後，再將用電腦訓練好的模型輸入至Android應用程式當中，使應用程式能夠在攝影的同時進行車禍判斷。

The invention of the vehicle shortens the distance between people, but it brings considerable danger. Car accidents will cause huge social costs and harm people's lives and property. Therefore, in recent years, research on car accidents has gradually increased. Taiwan is a densely populated country, and most people use motorcycles as their means of transportation. Thus, Taiwan's car accident casualty rate is one of the highest in the world. It is important to deal with problems related to car accidents in Taiwan. Most of the relevant literature, which analyzes the sensor information on the vehicle to determine whether there is a car accident, cannot preserve the circumstances, so it is impossible to judge the traffic accident responsibility. We use the deep learning object detection method, Faster Region-based Convolutional Neural Network(Faster R-CNN), to instantly identify car accidents. And we implement it on the Android system. We write apps through Android Studio, which can judge car accidents while photographing.

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