注：本文源自本人的硕士毕业论文，未经许可，严禁转载！
原文请参考知网：知网本论文下载地址
摘 要
随着计算机性能的飞速提升，蛰伏已久的深度学习算法终于迎来了高速发展的时期。物体识别（也叫物体检测，目标检测）是计算机视觉领域中最有价值的研究方向之一。本论文主要研究的是卷积神经网络算法在一般场景下物体识别方法的应用，更具体地说，这里的物体识别是指行车时路况信息（包括行人、过往车辆、信号灯等）的识别。
传统的物体识别方法分为三个步骤：首先在原始图像上生成目标建议框，然后提取这些建议框的特征，最后对框里的物体进行分类和边框回归。其中每一步都存在问题，近似于穷举式的目标建议框生成策略直接影响检测的速度、精准度和计算的冗余量；传统方法采用人工提取图像特征的方式并不能保证特征的质量；特征分类采用传统机器学习方法导致速度慢。更重要的一点是，这三个步骤是完全分离的，不能做到实时检测。
本论文针对上述三个问题，使用神经网络算法突破了问题的难点。首先针对手工提取图像特征难的问题，论文实现一个基于密集连接网络（DenseNet）改进的卷积神经网络，用于自动提取高质量的深度特征，能够替代手工提取图像特征；其次针对传统分类器速度慢的问题，论文使用Softmax分类器进行预测，该分类器能够天然地和卷积网络组合使用，这样就可以把第二、三步合并到一个网络中，大大提升了检测速度和精度；然后针对目标建议框生成策略，论文抛弃了直接在原始图像上生成建议框的方式，而是用神经网络首先提取图像特征，再在特征图上进行建议框生成策略，这样既准确又高效。最后，论文将以上解决方案应用到SSD检测方法中，生成一个改进的SSD检测算法。经过测试，改进后的SSD方法在检测速度和精度上均有明显的提升，并且在检测小目标时表现更佳。改进的SSD检测方法将三个检测过程合并到了同一个网络中，真正实现了端到端的实时检测。
本论文最后根据改进版的SSD检测算法实现了一个实时物体识别系统。得益于检测算法速度快、精度高等优点，系统最终实现了稳定实时检测的目标。
关键词：物体识别，卷积神经网络，密集连接，实时
ABSTRACT
With the rapid improvement of computer performance, long-standing deep learning algorithms have finally ushered in a period of rapid development. Object recognition (also called object detection, object detection) is one of the most valuable research directions in the field of computer vision. This dissertation focuses on the application of convolutional neural network algorithm in object recognition in general scenes. More specifically, object recognition here refers to the identification of traffic information (including pedestrians, passing vehicles, traffic lights, etc.) when the vehicle is traveling.
The traditional target detection method is divided into three steps. Firstly, the target suggestion frame is generated on the original image, then the features of these suggestion boxes are extracted, and finally the objects in the frame are classified and the frame is returned. Each step has its own problem, which is similar to the exhaustive target suggestion box generation strategy that directly affects the detection speed, accuracy, and computational redundancy. The traditional method of manually extracting image features does not guarantee the quality of the feature. Classification using traditional machine learning methods results in slow speeds. More importantly, these three steps are completely separate and cannot be used for real-time detection.
This paper aims at the above three problems, using neural network algorithm to break through the difficulties of the problem. For the difficulty of manually extracting image features, the paper implements a convolutional neural network based on dense connection network (DenseNet), which can automatically extract high-quality depth features and can replace the manual extraction of image features. For traditional classifiers, the speed is slow. The problem is that the paper uses the Softmax classifier to make predictions. The classifier can be used naturally in combination with a convolutional network, so that the second and third steps can be merged into one network, greatly improving the detection speed and accuracy; Generate strategy, the paper abandoned the way to generate the suggestion frame directly on the original image, but first use neural network to extract the image feature first, and then propose the strategy to generate the strategy on the feature map, which is accurate and efficient. Finally, the paper applies the above solution to the SSD detection method to generate an improved SSD detection algorithm. After testing, the improved SSD method has significantly improved detection speed and accuracy, and performs better when detecting small targets. The improved SSD detection method incorporates the three detection processes into the same network and truly enables end-to-end real-time detection.
At last, this paper implements a real-time object recognition (road condition information recognition) system based on the improved SSD detection algorithm. Thanks to the advantages of fast detection speed and high precision, the system finally achieves the goal of stable real-time detection.
Keywords: object recognition, convolutional neural networks, dense connections, real-time