本文選題：目標檢測 + 深度學習　； 參考：《首都經濟貿易大學》2017年碩士論文

【摘要】：計算機科學的飛速發展,給人類的生活帶來了很大的進步,使人類的生活變得越來越智能。人工智能一直是人類孜孜不倦探索得重要領域。眾所周知,人類的視覺是感知外部世界的重要組成部分,科學研究表明,人的百分之七八十的信息是通過視覺來感知到的。所以,在人類探索人工智能的漫漫長途中,計算機視覺一直都是一個重要的研究方向。計算機視覺涉及到圖像處理,機器學習,模式識別等多個學科,最終目的是為了模擬人的視覺,以便用計算機完成各種識別任務。其中,目標檢測是計算機視覺方向中非常重要的一個子方向。目標檢測主要是檢測出圖片中所關注的目標,例如,自動駕駛系統對于目標檢測的要求就是要檢測出當前行車環境中的行人、車輛等各種物體。由于真實路況的復雜性,要求檢測系統對于場景有著較高級別的語義理解。過去,大部分目標檢測算法基本是基于傳統的濾波方法,提取人工設計出來的經典特征,如SIFT[22],HOG[2],然后放入經典的分類器(如SVM[30]、Adaboost[29])進行分類識別。由于使用的是手工特征,所以魯棒性較差,而且工作量大,當環境出現明顯變化時,目標檢測效果的差異很大。由于深度學習中卷積神經網絡的極強的特征表達能力,提取的特征具有非常強的魯棒性,所以,本文主要是利用了基于深度學習的比較經典的檢測框架—Faster R-CNN[5],并在此基礎上嘗試著使用不同的特征提取層,在傳統經典模型的基礎上,對網絡結構進行了改變,使現在的網絡模型在精度和速度之間作了更好的權衡。并利用標定的數據對模型進行訓練,調節參數,最終訓練出一個精度和速度較好的模型,并應用到檢測系統中。本文的目標檢測系統的開發環境為Linux,利用專注圖像界面的Qt圖形界面庫作為界面的開發框架,底層使用了C++語言。本文中所描述的目標檢測系統開發過程主要包括整體的需求分析、總體的設計與實現和測試等。最后通過測試,證明系統在硬件和性能上都有著良好的表現。
[Abstract]:The rapid development of computer science brings great progress to human life and makes human life more intelligent. Artificial intelligence has always been an important field for human beings to explore tirelessly. As we all know, human vision is an important part of the perception of the external world. Scientific research shows that 70% of human information is perceived through vision. Therefore, computer vision has always been an important research direction in the long-distance exploration of artificial intelligence. Computer vision involves many subjects, such as image processing, machine learning, pattern recognition and so on. Among them, target detection is a very important sub-direction in the direction of computer vision. Target detection is mainly to detect the object concerned in the picture. For example, the requirement of automatic driving system for target detection is to detect all kinds of objects such as pedestrians, vehicles and so on in the current driving environment. Because of the complexity of the real road conditions, the detection system is required to have a higher level of semantic understanding of the scene. In the past, most of the target detection algorithms were based on traditional filtering methods to extract the classical features, such as SIFT [22] Hog [2], and then put them into classical classifiers (such as SVM [30] / Adaboost [29]) for classification and recognition. Because the manual feature is used, the robustness is poor, and the workload is large. When the environment changes obviously, the target detection effect is very different. Because of the strong feature expression ability of convolution neural network in deep learning, the extracted feature is very robust. In this paper, we mainly use the more classical detection framework based on depth learning-Faster R-CNN [5], and on this basis try to use different feature extraction layers, on the basis of the traditional classical model, the network structure is changed. So that the current network model to make a better balance between accuracy and speed. The calibration data is used to train the model and adjust the parameters. Finally, a model with good precision and speed is trained and applied to the detection system. The development environment of the target detection system in this paper is Linux. the QT graphical interface library which focuses on the image interface is used as the development framework of the interface, and C language is used in the bottom layer. The development process of the target detection system described in this paper mainly includes the whole requirement analysis, the overall design and implementation, and the test and so on. Finally, through the test, it is proved that the system has good performance in both hardware and performance.
【學位授予單位】：首都經濟貿易大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP18

【參考文獻】

相關碩士學位論文 前2條

1 李松澤;基于深度學習的車道線檢測系統的設計與實現[D];哈爾濱工業大學;2016年

2 王斌;基于深度學習的行人檢測[D];北京交通大學;2015年

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本文編號：1915711