基於深度學習之雨滴偵測及還原技術之研發;The Developments of the Deep learning-based Raindrop Detection and Inpainting Technologies

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Title:	基於深度學習之雨滴偵測及還原技術之研發;The Developments of the Deep learning-based Raindrop Detection and Inpainting Technologies
Authors:	張鈞翔;Chang, Chun-Hsiang
Contributors:	資訊工程學系
Keywords:	生成對抗神經網路;深度學習;車道線偵測;影像復原;影像辨識應用;GAN;Deep Learning;Lan Detection;Image Inpainting;Image Recognition Application
Date:	2022-08-12
Issue Date:	2022-10-04 12:05:16 (UTC+8)
Publisher:	國立中央大學
Abstract:	四面環海的台灣，即使面積只有 36197 平方公里，但平均年降雨量相當大，為 2515 毫米，是世界平均雨量的 3 倍之多，台灣地理位置位於季風氣候範圍內，並且也是太平洋生成颱風的路徑上，每年平均有 3 到 4 次颱風侵襲，如此大量的降雨會引起許多災害，也會造成車輛電子設備的失準。隨著先進車輛輔助系統日益發達，我們的日常行車有了許多科技的支援，不僅如傳統雷達偵測，針對四周障礙及車輛的危險偵測，近年來更加入了深度學習的技術偵測車道線，確保車子行駛在正確的道路上，如果駕駛人可能偶爾不專心或其他因素分心，此系統可預先警示駕駛者道路狀況或自動控制車輛路徑，使交通事故發生率大幅降低。除了預防危險系統，近年由於電腦視覺及深度學習的發展，越來越多影像辨識應用在車輛行駛上，許多車輛將速限辨識系統整合進抬頭顯示器中，駕駛人即可專心於道路狀況，不須時刻分心注意是否超速，提供便利的同時，也增加行車安全。然而，若行駛於雨天或充滿泥淖的環境，可能會因為鏡頭被遮擋而使辨識能力降低、喪失，更甚者會影響行車安全。因此本篇論文針對白天的街景圖生成模擬雨滴遮擋鏡頭的問題，使用生成對抗網路還原被雨滴遮擋的影像，以提升車道線偵測準確率，從原本被雨滴遮擋後的 0.916 提升到影像還原後的 0.933，以及提升招牌偵測的準確度，進而降低誤判的機率，進而提升行車安全。;Taiwan is surrounded by sea on all sides. Even though the area is only 36,197 square kilometers, the average annual rainfall is is 2515 mm. It is three times more than the world’s average rainfall. Taiwan is located in the range of monsoon climate, and it is also on the path of the typhoons generated in the Pacific Ocean. There are an average of 3 to 4 typhoons each year, and such a large amount of rain will cause many disasters. It can also cause misalignment of vehicle electronics. With the increasing development of advanced vehicle assistance systems, Our daily driving is supported by many technologies. Not only is traditional radar detection and hazard detection for surrounding obstacles and vehicles, but the technology of deep learning has been also used to detect lane lines in recent years. If the driver may be occasionally inattentive or distracted, this system can warn the driver of road conditions in advance or automatically control the vehicle path. As a result, the incidence of traffic accidents is greatly reduced. Besides preventing danger systems, more and more image recognition applications are used in vehicle driving because of the development of computer vision and deep learning in recent years. Many cars have integrated the speed limit recognition system into the head-up display. With the help of the system, the driver doesn’t need to be distracted by seeing if they are speeding and conceniii trates on the road conditions. While providing convenience, the head-up display system also increases driving safety. If the diver drives in rainy or muddy environment, the recognition ability of advanced vehicle assistance systems may be reduced because the camera lens is blocked. What’s more, it will affect the driving safety. Therefore, this paper aims at the problem of raindrops blocking the lens pn the sunny day. We improve the accuracy of lane line detection from 0.916 to 0.933 after restoring the images being blocked by artificial raindrops using Generative Adversarial Networks. As a result, our method reduces the probability of misjudgment of the advanced vehicle assistance system and improves driving safety.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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