利用深度學習提升夜間行駛車輛的辨識能力

Enhancing Nighttime Vehicle Recognition with Deep Learning

傳統的夜間監控系統受限於光線不足，導致車輛識別率較低。然而，本研究所提出的方法結合了可見光和熱感影像，大幅提高了夜間駕駛車輛的識別率。我們運用Yolo算法對這些混合圖像中的車輛進行準確識別，不僅提高了辨識的準確性，也提供了更豐富的交通流資訊。
這項方法不僅僅是技術層面的創新，更在夜間交通監測方面具有實際的應用價值。透過我們的方法，交通管理者可以更精準地了解夜間路段的車輛密度、速度等信息，為交通管理和安全決策提供了更可靠的數據支持。
本研究的實驗結果顯示，所提出的方法在車輛識別中實現了高精度。這不僅使得夜間駕駛更加安全，也為城市交通管理提供了一個具有前景的解決方案。這種綜合應用不僅為夜間行車帶來更高的安全性，同時也為智慧城市的建設提供了更可靠的技術支持。

Traditional nighttime surveillance systems are limited by low light conditions, resulting in lower vehicle recognition rates. However, the method proposed in this study combines visible light and thermal imaging, significantly increasing the recognition rate of driving vehicles at night. We applied the Yolo algorithm to accurately identify vehicles in these hybrid images, not only improving recognition accuracy but also providing richer traffic flow information.
This approach is not only a technical innovation but also has practical application value in nighttime traffic monitoring. Through our method, traffic managers can gain a more precise understanding of nighttime road segments, including vehicle density, speed, and other information, providing more reliable data support for traffic management and safety decisions.
The experimental results of this study demonstrate high accuracy in vehicle recognition. This not only enhances the safety of nighttime driving but also provides a promising solution for urban traffic management. This integrated application not only brings higher security to nighttime driving but also provides more reliable technical support for the construction of smart cities.