Knowledge Management System Of Institute of optics and electronics, CAS
|Place of Conferral||中国科学院光电技术研究所|
|Keyword||小目标检测 卷积神经网络 语义增强 扩展层预测 正则化|
Object recognition and detection is an important research issue in the fields of computer vision, multimedia application and others. The task of object recognition is to judge whether a specified object is included for a given input image. For multi-class objects detection, it is also required to output the category of every object.
In addition, the other fundamental task of object detection is to identify the accurate location of objects in image. Nowadays, a huge amount of image data was generated for the rapid development of image acquisition and transmission technology. The technology of automatically recognizing objects in images plays an important role in many applications in tasks of high-level visual processing and analysis such as behavior analysis and semantic understanding. In addition, object detection technology has been widely used in real-time monitoring of urban public security and road detection in aerial images from drones and satellites. Although many breakthroughs have been made, object detection still faces with various challenges. For instance, it is difficult to detect the object accurately under circumstances of complicated background, uneven illumination, blurred or small-size objects. The application of convolutional neural networks increases model complexity, which leads to a dramatic increase in computational cost. Object recognition and detection algorithm with high real-time, accuracy and stability has become a hot research topic.
Around the issue of precise detection of small-scale objects on the ground, this paper conducted some researches combining with related algorithms of computer vision, machine learning. The specific research contents include: 1.research on the ground small-size objects detection on the condition of different light and various scales, from the angle of feature extraction and expression, prediction frame generation, and improvement of positioning accuracy. The main contributions of this paper are as follows:
Firstly, the current object detection algorithms based on deep learning have performed well on objects of regular sizes. However, detection effect of small-size objects on the ground is not ideal, as the small objects with less appearance information are vulnerable to the interference of background, which leads to the difficulties of object detection. This paper compared several classical object detection frameworks based on convolutional neural networks on small objects detection. From the perspective of object feature extraction, this paper proposed a sub-network based on feature fusion to acquire enhanced semantic feature of small objects. The network has utilized information of feature maps from multiple levels of depth to obtain the fusion feature, which was used as input to the detection network. Many detection algorithms only use the information of high-level layer features, ignoring the local detail information which is critical to small objects. By comparing with these algorithms, our method effectively enhances the feature representation of small objects.
Secondly, aiming at the objects in images of various scales and angles, this paper designs an extensional prediction network. The object is matched in multiple scale spaces of the extensional layers. The error between of the predicted value of each level and the ground truth value is used as the loss to train our model, which effectively improves the detection accuracy of the small-sized object.
Finally, although models based on deep learning of object detection have powerful representation ability, however, the increase in complexity of the model is easy to cause the problem of over-fitting. This paper has made a series of improvements to improve the generalization ability of the model from the angle of increasing the size of training dataset and reducing the complexity of the model, which included dataset augmentation, BN layer normalization and L2-regularization. These methods further improved the detection accuracy of our model, and accelerated the speed of model convergence. Experiments were conducted with two public small-sized object datasets, which confirmed that the model proposed in this paper shows obvious advantages.
|MOST Discipline Catalogue||工学|
|李小宁. 基于深度学习的对地目标检测技术研究[D]. 中国科学院光电技术研究所. 中国科学院大学,2019.|
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|李小宁-基于深度学习的对地目标检测技术研（3338KB）||学位论文||开放获取||CC BY-NC-SA||Application Full Text|
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